A hybrid machine learning approach to hotel sales rank prediction

Gespeichert in:

Autor*in:	Srivastava, Praveen Ranjan [verfasserIn] Eachempati, Prajwal [verfasserIn] Charles, Vincent [verfasserIn] Rana, Nripendra P. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Sentiment analysis ANN predictive model regression analysis sales rank prediction

Übergeordnetes Werk:	Enthalten in: Journal of the Operational Research Society - Operational Research Society, London : Taylor and Francis, 1978, 74(2023), 6, Seite 1407-1423
Übergeordnetes Werk:	volume:74 ; year:2023 ; number:6 ; pages:1407-1423

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1080/01605682.2022.2096498

Katalog-ID:	1858782082

Internformat


LEADER	01000caa a2200265 4500
001	1858782082
003	DE-627
005	20240122145548.0
007	cr uuu---uuuuu
008	230905s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1080/01605682.2022.2096498 \|2 doi
035			\|a (DE-627)1858782082
035			\|a (DE-599)KXP1858782082
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
100	1		\|a Srivastava, Praveen Ranjan \|e verfasserin \|0 (DE-588)1140075780 \|0 (DE-627)898200490 \|0 (DE-576)493641688 \|4 aut
245	1	2	\|a A hybrid machine learning approach to hotel sales rank prediction \|c Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles, Nripendra P. Rana
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
650		4	\|a Sentiment analysis \|7 (dpeaa)DE-206
650		4	\|a ANN \|7 (dpeaa)DE-206
650		4	\|a predictive model \|7 (dpeaa)DE-206
650		4	\|a regression analysis \|7 (dpeaa)DE-206
650		4	\|a sales rank prediction \|7 (dpeaa)DE-206
700	1		\|a Eachempati, Prajwal \|e verfasserin \|4 aut
700	1		\|a Charles, Vincent \|e verfasserin \|0 (DE-588)1051525624 \|0 (DE-627)786386398 \|0 (DE-576)406609551 \|4 aut
700	1		\|a Rana, Nripendra P. \|e verfasserin \|0 (DE-588)1175514764 \|0 (DE-627)1046499920 \|0 (DE-576)516284223 \|4 aut
773	0	8	\|i Enthalten in \|a Operational Research Society \|t Journal of the Operational Research Society \|d London : Taylor and Francis, 1978 \|g 74(2023), 6, Seite 1407-1423 \|h Online-Ressource \|w (DE-627)320465098 \|w (DE-600)2007775-0 \|w (DE-576)103939180 \|x 1476-9360 \|7 nnns
773	1	8	\|g volume:74 \|g year:2023 \|g number:6 \|g pages:1407-1423
856	4	0	\|u https://www.tandfonline.com/doi/pdf/10.1080/01605682.2022.2096498 \|x Verlag \|z kostenfrei
856	4	0	\|u https://doi.org/10.1080/01605682.2022.2096498 \|x Resolving-System \|z kostenfrei
912			\|a GBV_USEFLAG_U
912			\|a GBV_ILN_26
912			\|a ISIL_DE-206
912			\|a SYSFLAG_1
912			\|a GBV_KXP
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_31
912			\|a GBV_ILN_63
912			\|a GBV_ILN_70
912			\|a GBV_ILN_100
912			\|a GBV_ILN_224
912			\|a GBV_ILN_285
912			\|a GBV_ILN_370
912			\|a GBV_ILN_374
912			\|a GBV_ILN_647
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2018
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_2935
912			\|a GBV_ILN_2940
912			\|a GBV_ILN_2949
912			\|a GBV_ILN_2950
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4346
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
912			\|a GBV_ILN_2403
912			\|a GBV_ILN_2403
912			\|a ISIL_DE-LFER
951			\|a AR
952			\|d 74 \|j 2023 \|e 6 \|h 1407-1423
980			\|2 26 \|1 01 \|x 0206 \|b 4372776209 \|y x1z \|z 05-09-23
980			\|2 2403 \|1 01 \|x DE-LFER \|b 4377734857 \|c 00 \|f --%%-- \|d --%%-- \|e n \|j --%%-- \|y l01 \|z 19-09-23
981			\|2 2403 \|1 01 \|x DE-LFER \|r https://doi.org/10.1080/01605682.2022.2096498
982			\|2 26 \|1 00 \|x DE-206 \|b One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.

Indexfelder

author_variant	p r s pr prs p e pe v c vc n p r np npr
matchkey_str	article:14769360:2023----::hbimcieerigprahooesls
hierarchy_sort_str	2023
publishDate	2023
allfields	10.1080/01605682.2022.2096498 doi (DE-627)1858782082 (DE-599)KXP1858782082 DE-627 ger DE-627 rda eng Srivastava, Praveen Ranjan verfasserin (DE-588)1140075780 (DE-627)898200490 (DE-576)493641688 aut A hybrid machine learning approach to hotel sales rank prediction Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles, Nripendra P. Rana 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sentiment analysis (dpeaa)DE-206 ANN (dpeaa)DE-206 predictive model (dpeaa)DE-206 regression analysis (dpeaa)DE-206 sales rank prediction (dpeaa)DE-206 Eachempati, Prajwal verfasserin aut Charles, Vincent verfasserin (DE-588)1051525624 (DE-627)786386398 (DE-576)406609551 aut Rana, Nripendra P. verfasserin (DE-588)1175514764 (DE-627)1046499920 (DE-576)516284223 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 74(2023), 6, Seite 1407-1423 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:74 year:2023 number:6 pages:1407-1423 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2022.2096498 Verlag kostenfrei https://doi.org/10.1080/01605682.2022.2096498 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 74 2023 6 1407-1423 26 01 0206 4372776209 x1z 05-09-23 2403 01 DE-LFER 4377734857 00 --%%-- --%%-- n --%%-- l01 19-09-23 2403 01 DE-LFER https://doi.org/10.1080/01605682.2022.2096498 26 00 DE-206 One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.
spelling	10.1080/01605682.2022.2096498 doi (DE-627)1858782082 (DE-599)KXP1858782082 DE-627 ger DE-627 rda eng Srivastava, Praveen Ranjan verfasserin (DE-588)1140075780 (DE-627)898200490 (DE-576)493641688 aut A hybrid machine learning approach to hotel sales rank prediction Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles, Nripendra P. Rana 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sentiment analysis (dpeaa)DE-206 ANN (dpeaa)DE-206 predictive model (dpeaa)DE-206 regression analysis (dpeaa)DE-206 sales rank prediction (dpeaa)DE-206 Eachempati, Prajwal verfasserin aut Charles, Vincent verfasserin (DE-588)1051525624 (DE-627)786386398 (DE-576)406609551 aut Rana, Nripendra P. verfasserin (DE-588)1175514764 (DE-627)1046499920 (DE-576)516284223 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 74(2023), 6, Seite 1407-1423 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:74 year:2023 number:6 pages:1407-1423 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2022.2096498 Verlag kostenfrei https://doi.org/10.1080/01605682.2022.2096498 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 74 2023 6 1407-1423 26 01 0206 4372776209 x1z 05-09-23 2403 01 DE-LFER 4377734857 00 --%%-- --%%-- n --%%-- l01 19-09-23 2403 01 DE-LFER https://doi.org/10.1080/01605682.2022.2096498 26 00 DE-206 One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.
allfields_unstemmed	10.1080/01605682.2022.2096498 doi (DE-627)1858782082 (DE-599)KXP1858782082 DE-627 ger DE-627 rda eng Srivastava, Praveen Ranjan verfasserin (DE-588)1140075780 (DE-627)898200490 (DE-576)493641688 aut A hybrid machine learning approach to hotel sales rank prediction Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles, Nripendra P. Rana 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sentiment analysis (dpeaa)DE-206 ANN (dpeaa)DE-206 predictive model (dpeaa)DE-206 regression analysis (dpeaa)DE-206 sales rank prediction (dpeaa)DE-206 Eachempati, Prajwal verfasserin aut Charles, Vincent verfasserin (DE-588)1051525624 (DE-627)786386398 (DE-576)406609551 aut Rana, Nripendra P. verfasserin (DE-588)1175514764 (DE-627)1046499920 (DE-576)516284223 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 74(2023), 6, Seite 1407-1423 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:74 year:2023 number:6 pages:1407-1423 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2022.2096498 Verlag kostenfrei https://doi.org/10.1080/01605682.2022.2096498 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 74 2023 6 1407-1423 26 01 0206 4372776209 x1z 05-09-23 2403 01 DE-LFER 4377734857 00 --%%-- --%%-- n --%%-- l01 19-09-23 2403 01 DE-LFER https://doi.org/10.1080/01605682.2022.2096498 26 00 DE-206 One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.
allfieldsGer	10.1080/01605682.2022.2096498 doi (DE-627)1858782082 (DE-599)KXP1858782082 DE-627 ger DE-627 rda eng Srivastava, Praveen Ranjan verfasserin (DE-588)1140075780 (DE-627)898200490 (DE-576)493641688 aut A hybrid machine learning approach to hotel sales rank prediction Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles, Nripendra P. Rana 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sentiment analysis (dpeaa)DE-206 ANN (dpeaa)DE-206 predictive model (dpeaa)DE-206 regression analysis (dpeaa)DE-206 sales rank prediction (dpeaa)DE-206 Eachempati, Prajwal verfasserin aut Charles, Vincent verfasserin (DE-588)1051525624 (DE-627)786386398 (DE-576)406609551 aut Rana, Nripendra P. verfasserin (DE-588)1175514764 (DE-627)1046499920 (DE-576)516284223 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 74(2023), 6, Seite 1407-1423 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:74 year:2023 number:6 pages:1407-1423 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2022.2096498 Verlag kostenfrei https://doi.org/10.1080/01605682.2022.2096498 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 74 2023 6 1407-1423 26 01 0206 4372776209 x1z 05-09-23 2403 01 DE-LFER 4377734857 00 --%%-- --%%-- n --%%-- l01 19-09-23 2403 01 DE-LFER https://doi.org/10.1080/01605682.2022.2096498 26 00 DE-206 One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.
allfieldsSound	10.1080/01605682.2022.2096498 doi (DE-627)1858782082 (DE-599)KXP1858782082 DE-627 ger DE-627 rda eng Srivastava, Praveen Ranjan verfasserin (DE-588)1140075780 (DE-627)898200490 (DE-576)493641688 aut A hybrid machine learning approach to hotel sales rank prediction Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles, Nripendra P. Rana 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sentiment analysis (dpeaa)DE-206 ANN (dpeaa)DE-206 predictive model (dpeaa)DE-206 regression analysis (dpeaa)DE-206 sales rank prediction (dpeaa)DE-206 Eachempati, Prajwal verfasserin aut Charles, Vincent verfasserin (DE-588)1051525624 (DE-627)786386398 (DE-576)406609551 aut Rana, Nripendra P. verfasserin (DE-588)1175514764 (DE-627)1046499920 (DE-576)516284223 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 74(2023), 6, Seite 1407-1423 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:74 year:2023 number:6 pages:1407-1423 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2022.2096498 Verlag kostenfrei https://doi.org/10.1080/01605682.2022.2096498 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 74 2023 6 1407-1423 26 01 0206 4372776209 x1z 05-09-23 2403 01 DE-LFER 4377734857 00 --%%-- --%%-- n --%%-- l01 19-09-23 2403 01 DE-LFER https://doi.org/10.1080/01605682.2022.2096498 26 00 DE-206 One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.
language	English
source	Enthalten in Journal of the Operational Research Society 74(2023), 6, Seite 1407-1423 volume:74 year:2023 number:6 pages:1407-1423
sourceStr	Enthalten in Journal of the Operational Research Society 74(2023), 6, Seite 1407-1423 volume:74 year:2023 number:6 pages:1407-1423
format_phy_str_mv	Article
building	26:1 2403:0
institution	findex.gbv.de
selectbib_iln_str_mv	26@1z 2403@01
topic_facet	Sentiment analysis ANN predictive model regression analysis sales rank prediction
sw_local_iln_str_mv	26: DE-206:
isfreeaccess_bool	true
container_title	Journal of the Operational Research Society
authorswithroles_txt_mv	Srivastava, Praveen Ranjan @@aut@@ Eachempati, Prajwal @@aut@@ Charles, Vincent @@aut@@ Rana, Nripendra P. @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320465098
id	1858782082
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1858782082</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240122145548.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230905s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/01605682.2022.2096498</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1858782082</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1858782082</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Srivastava, Praveen Ranjan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1140075780</subfield><subfield code="0">(DE-627)898200490</subfield><subfield code="0">(DE-576)493641688</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A hybrid machine learning approach to hotel sales rank prediction</subfield><subfield code="c">Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles, Nripendra P. Rana</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sentiment analysis</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ANN</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">predictive model</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">regression analysis</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sales rank prediction</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Eachempati, Prajwal</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Charles, Vincent</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1051525624</subfield><subfield code="0">(DE-627)786386398</subfield><subfield code="0">(DE-576)406609551</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rana, Nripendra P.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1175514764</subfield><subfield code="0">(DE-627)1046499920</subfield><subfield code="0">(DE-576)516284223</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="a">Operational Research Society</subfield><subfield code="t">Journal of the Operational Research Society</subfield><subfield code="d">London : Taylor and Francis, 1978</subfield><subfield code="g">74(2023), 6, Seite 1407-1423</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320465098</subfield><subfield code="w">(DE-600)2007775-0</subfield><subfield code="w">(DE-576)103939180</subfield><subfield code="x">1476-9360</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:74</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:6</subfield><subfield code="g">pages:1407-1423</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.tandfonline.com/doi/pdf/10.1080/01605682.2022.2096498</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1080/01605682.2022.2096498</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_374</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_647</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2935</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2940</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2949</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2950</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4346</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">74</subfield><subfield code="j">2023</subfield><subfield code="e">6</subfield><subfield code="h">1407-1423</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4372776209</subfield><subfield code="y">x1z</subfield><subfield code="z">05-09-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="b">4377734857</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">19-09-23</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.1080/01605682.2022.2096498</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.</subfield></datafield></record></collection>
standort_str_mv	--%%--
standort_iln_str_mv	2403:--%%-- DE-LFER:--%%--
author	Srivastava, Praveen Ranjan
spellingShingle	Srivastava, Praveen Ranjan misc Sentiment analysis misc ANN misc predictive model misc regression analysis misc sales rank prediction A hybrid machine learning approach to hotel sales rank prediction
authorStr	Srivastava, Praveen Ranjan
ppnlink_with_tag_str_mv	@@773@@(DE-627)320465098
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut
typewithnormlink_str_mv	Person@(DE-588)1140075780 DifferentiatedPerson@(DE-588)1140075780 DifferentiatedPerson@(DE-588)1051525624 Person@(DE-588)1051525624 DifferentiatedPerson@(DE-588)1175514764 Person@(DE-588)1175514764
collection	KXP GVK SWB
remote_str	true
last_changed_iln_str_mv	26@05-09-23 2403@19-09-23
illustrated	Not Illustrated
issn	1476-9360
topic_title	26 00 DE-206 One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided A hybrid machine learning approach to hotel sales rank prediction Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles, Nripendra P. Rana Sentiment analysis (dpeaa)DE-206 ANN (dpeaa)DE-206 predictive model (dpeaa)DE-206 regression analysis (dpeaa)DE-206 sales rank prediction (dpeaa)DE-206
topic	misc Sentiment analysis misc ANN misc predictive model misc regression analysis misc sales rank prediction
topic_unstemmed	misc Sentiment analysis misc ANN misc predictive model misc regression analysis misc sales rank prediction
topic_browse	misc Sentiment analysis misc ANN misc predictive model misc regression analysis misc sales rank prediction
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
standort_txtP_mv	--%%--
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Journal of the Operational Research Society
normlinkwithtype_str_mv	(DE-588)1140075780@Person (DE-588)1140075780@DifferentiatedPerson (DE-588)1051525624@DifferentiatedPerson (DE-588)1051525624@Person (DE-588)1175514764@DifferentiatedPerson (DE-588)1175514764@Person
hierarchy_parent_id	320465098
signature	--%%--
signature_str_mv	--%%--
hierarchy_top_title	Journal of the Operational Research Society
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180
normlinkwithrole_str_mv	(DE-588)1140075780@@aut@@ (DE-588)1051525624@@aut@@ (DE-588)1175514764@@aut@@
title	A hybrid machine learning approach to hotel sales rank prediction
ctrlnum	(DE-627)1858782082 (DE-599)KXP1858782082
title_full	A hybrid machine learning approach to hotel sales rank prediction Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles, Nripendra P. Rana
author_sort	Srivastava, Praveen Ranjan
journal	Journal of the Operational Research Society
journalStr	Journal of the Operational Research Society
callnumber-first-code	-
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
container_start_page	1407
author_browse	Srivastava, Praveen Ranjan Eachempati, Prajwal Charles, Vincent Rana, Nripendra P.
selectkey	26:x 2403:l
container_volume	74
format_se	Elektronische Aufsätze
author-letter	Srivastava, Praveen Ranjan
doi_str_mv	10.1080/01605682.2022.2096498
normlink	1140075780 898200490 493641688 1051525624 786386398 406609551 1175514764 1046499920 516284223
normlink_prefix_str_mv	(DE-588)1140075780 (DE-627)898200490 (DE-576)493641688 (DE-588)1051525624 (DE-627)786386398 (DE-576)406609551 (DE-588)1175514764 (DE-627)1046499920 (DE-576)516284223
author2-role	verfasserin
title_sort	hybrid machine learning approach to hotel sales rank prediction
title_auth	A hybrid machine learning approach to hotel sales rank prediction
collection_details	GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 ISIL_DE-LFER
container_issue	6
title_short	A hybrid machine learning approach to hotel sales rank prediction
url	https://www.tandfonline.com/doi/pdf/10.1080/01605682.2022.2096498 https://doi.org/10.1080/01605682.2022.2096498
ausleihindikator_str_mv	26 2403:n
rolewithnormlink_str_mv	@@aut@@(DE-588)1140075780 @@aut@@(DE-588)1051525624 @@aut@@(DE-588)1175514764
remote_bool	true
author2	Eachempati, Prajwal Charles, Vincent Rana, Nripendra P.
author2Str	Eachempati, Prajwal Charles, Vincent Rana, Nripendra P.
ppnlink	320465098
GND_str_mv	Srivastava, Praveen Ranjan Srivastava, Praveen Srivastava, Praveen Ranjan Charles, V. Charles, Vincent Rana, Nripendra P. Rana, Nripendra Rana, Nripendra P.
GND_txt_mv	Srivastava, Praveen Ranjan Srivastava, Praveen Srivastava, Praveen Ranjan Charles, V. Charles, Vincent Rana, Nripendra P. Rana, Nripendra Rana, Nripendra P.
GND_txtF_mv	Srivastava, Praveen Ranjan Srivastava, Praveen Srivastava, Praveen Ranjan Charles, V. Charles, Vincent Rana, Nripendra P. Rana, Nripendra Rana, Nripendra P.
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1080/01605682.2022.2096498
callnumber-a	--%%--
up_date	2024-07-04T12:12:05.236Z
_version_	1803650466041561088
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1858782082</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240122145548.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230905s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/01605682.2022.2096498</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1858782082</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1858782082</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Srivastava, Praveen Ranjan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1140075780</subfield><subfield code="0">(DE-627)898200490</subfield><subfield code="0">(DE-576)493641688</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A hybrid machine learning approach to hotel sales rank prediction</subfield><subfield code="c">Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles, Nripendra P. Rana</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sentiment analysis</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ANN</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">predictive model</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">regression analysis</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sales rank prediction</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Eachempati, Prajwal</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Charles, Vincent</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1051525624</subfield><subfield code="0">(DE-627)786386398</subfield><subfield code="0">(DE-576)406609551</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rana, Nripendra P.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1175514764</subfield><subfield code="0">(DE-627)1046499920</subfield><subfield code="0">(DE-576)516284223</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="a">Operational Research Society</subfield><subfield code="t">Journal of the Operational Research Society</subfield><subfield code="d">London : Taylor and Francis, 1978</subfield><subfield code="g">74(2023), 6, Seite 1407-1423</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320465098</subfield><subfield code="w">(DE-600)2007775-0</subfield><subfield code="w">(DE-576)103939180</subfield><subfield code="x">1476-9360</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:74</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:6</subfield><subfield code="g">pages:1407-1423</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.tandfonline.com/doi/pdf/10.1080/01605682.2022.2096498</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1080/01605682.2022.2096498</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_374</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_647</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2935</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2940</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2949</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2950</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4346</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">74</subfield><subfield code="j">2023</subfield><subfield code="e">6</subfield><subfield code="h">1407-1423</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4372776209</subfield><subfield code="y">x1z</subfield><subfield code="z">05-09-23</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="b">4377734857</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">19-09-23</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.1080/01605682.2022.2096498</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.</subfield></datafield></record></collection>
score	7.4013834

Nicht das Richtige dabei?

Schreiben Sie uns!

A hybrid machine learning approach to hotel sales rank prediction

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?