A new neighbourhood-based diffusion algorithm for personalized recommendation
Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neig...
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Gespeichert in:
| Autor*in: |
Mumin, Diyawu [verfasserIn] Shi, Lei-Lei [verfasserIn] Liu, Lu [verfasserIn] Han, Zi-xuan [verfasserIn] Jiang, Liang [verfasserIn] Wu, Yan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
Neighbourhood-based recommendation |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Knowledge and information systems - Springer London, 1999, 66(2024), 9 vom: 16. Mai, Seite 5389-5408 |
|---|---|
| Übergeordnetes Werk: |
volume:66 ; year:2024 ; number:9 ; day:16 ; month:05 ; pages:5389-5408 |
| Links: |
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| DOI / URN: |
10.1007/s10115-024-02127-1 |
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| Katalog-ID: |
SPR056951590 |
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| 520 | |a Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms. | ||
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| 650 | 4 | |a Diffusion-based recommendation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Item recommendation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Shi, Lei-Lei |e verfasserin |0 (orcid)0000-0003-2780-5602 |4 aut | |
| 700 | 1 | |a Liu, Lu |e verfasserin |0 (orcid)0000-0003-1013-4507 |4 aut | |
| 700 | 1 | |a Han, Zi-xuan |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Yan |e verfasserin |4 aut | |
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10.1007/s10115-024-02127-1 doi (DE-627)SPR056951590 (SPR)s10115-024-02127-1-e DE-627 ger DE-627 rakwb eng 004 VZ 004 070 VZ 06.74 bkl 54.64 bkl Mumin, Diyawu verfasserin (orcid)0000-0003-4941-5197 aut A new neighbourhood-based diffusion algorithm for personalized recommendation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms. Recommender system (dpeaa)DE-He213 Personalized recommendation (dpeaa)DE-He213 Neighbourhood-based recommendation (dpeaa)DE-He213 Diffusion-based recommendation (dpeaa)DE-He213 Item recommendation (dpeaa)DE-He213 Shi, Lei-Lei verfasserin (orcid)0000-0003-2780-5602 aut Liu, Lu verfasserin (orcid)0000-0003-1013-4507 aut Han, Zi-xuan verfasserin aut Jiang, Liang verfasserin aut Wu, Yan verfasserin aut Enthalten in Knowledge and information systems Springer London, 1999 66(2024), 9 vom: 16. Mai, Seite 5389-5408 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:66 year:2024 number:9 day:16 month:05 pages:5389-5408 https://dx.doi.org/10.1007/s10115-024-02127-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 06.74 VZ 54.64 VZ AR 66 2024 9 16 05 5389-5408 |
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10.1007/s10115-024-02127-1 doi (DE-627)SPR056951590 (SPR)s10115-024-02127-1-e DE-627 ger DE-627 rakwb eng 004 VZ 004 070 VZ 06.74 bkl 54.64 bkl Mumin, Diyawu verfasserin (orcid)0000-0003-4941-5197 aut A new neighbourhood-based diffusion algorithm for personalized recommendation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms. Recommender system (dpeaa)DE-He213 Personalized recommendation (dpeaa)DE-He213 Neighbourhood-based recommendation (dpeaa)DE-He213 Diffusion-based recommendation (dpeaa)DE-He213 Item recommendation (dpeaa)DE-He213 Shi, Lei-Lei verfasserin (orcid)0000-0003-2780-5602 aut Liu, Lu verfasserin (orcid)0000-0003-1013-4507 aut Han, Zi-xuan verfasserin aut Jiang, Liang verfasserin aut Wu, Yan verfasserin aut Enthalten in Knowledge and information systems Springer London, 1999 66(2024), 9 vom: 16. Mai, Seite 5389-5408 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:66 year:2024 number:9 day:16 month:05 pages:5389-5408 https://dx.doi.org/10.1007/s10115-024-02127-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 06.74 VZ 54.64 VZ AR 66 2024 9 16 05 5389-5408 |
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10.1007/s10115-024-02127-1 doi (DE-627)SPR056951590 (SPR)s10115-024-02127-1-e DE-627 ger DE-627 rakwb eng 004 VZ 004 070 VZ 06.74 bkl 54.64 bkl Mumin, Diyawu verfasserin (orcid)0000-0003-4941-5197 aut A new neighbourhood-based diffusion algorithm for personalized recommendation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms. Recommender system (dpeaa)DE-He213 Personalized recommendation (dpeaa)DE-He213 Neighbourhood-based recommendation (dpeaa)DE-He213 Diffusion-based recommendation (dpeaa)DE-He213 Item recommendation (dpeaa)DE-He213 Shi, Lei-Lei verfasserin (orcid)0000-0003-2780-5602 aut Liu, Lu verfasserin (orcid)0000-0003-1013-4507 aut Han, Zi-xuan verfasserin aut Jiang, Liang verfasserin aut Wu, Yan verfasserin aut Enthalten in Knowledge and information systems Springer London, 1999 66(2024), 9 vom: 16. Mai, Seite 5389-5408 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:66 year:2024 number:9 day:16 month:05 pages:5389-5408 https://dx.doi.org/10.1007/s10115-024-02127-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 06.74 VZ 54.64 VZ AR 66 2024 9 16 05 5389-5408 |
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10.1007/s10115-024-02127-1 doi (DE-627)SPR056951590 (SPR)s10115-024-02127-1-e DE-627 ger DE-627 rakwb eng 004 VZ 004 070 VZ 06.74 bkl 54.64 bkl Mumin, Diyawu verfasserin (orcid)0000-0003-4941-5197 aut A new neighbourhood-based diffusion algorithm for personalized recommendation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms. Recommender system (dpeaa)DE-He213 Personalized recommendation (dpeaa)DE-He213 Neighbourhood-based recommendation (dpeaa)DE-He213 Diffusion-based recommendation (dpeaa)DE-He213 Item recommendation (dpeaa)DE-He213 Shi, Lei-Lei verfasserin (orcid)0000-0003-2780-5602 aut Liu, Lu verfasserin (orcid)0000-0003-1013-4507 aut Han, Zi-xuan verfasserin aut Jiang, Liang verfasserin aut Wu, Yan verfasserin aut Enthalten in Knowledge and information systems Springer London, 1999 66(2024), 9 vom: 16. Mai, Seite 5389-5408 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:66 year:2024 number:9 day:16 month:05 pages:5389-5408 https://dx.doi.org/10.1007/s10115-024-02127-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 06.74 VZ 54.64 VZ AR 66 2024 9 16 05 5389-5408 |
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10.1007/s10115-024-02127-1 doi (DE-627)SPR056951590 (SPR)s10115-024-02127-1-e DE-627 ger DE-627 rakwb eng 004 VZ 004 070 VZ 06.74 bkl 54.64 bkl Mumin, Diyawu verfasserin (orcid)0000-0003-4941-5197 aut A new neighbourhood-based diffusion algorithm for personalized recommendation 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms. Recommender system (dpeaa)DE-He213 Personalized recommendation (dpeaa)DE-He213 Neighbourhood-based recommendation (dpeaa)DE-He213 Diffusion-based recommendation (dpeaa)DE-He213 Item recommendation (dpeaa)DE-He213 Shi, Lei-Lei verfasserin (orcid)0000-0003-2780-5602 aut Liu, Lu verfasserin (orcid)0000-0003-1013-4507 aut Han, Zi-xuan verfasserin aut Jiang, Liang verfasserin aut Wu, Yan verfasserin aut Enthalten in Knowledge and information systems Springer London, 1999 66(2024), 9 vom: 16. Mai, Seite 5389-5408 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:66 year:2024 number:9 day:16 month:05 pages:5389-5408 https://dx.doi.org/10.1007/s10115-024-02127-1 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-BBI GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 06.74 VZ 54.64 VZ AR 66 2024 9 16 05 5389-5408 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). 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a new neighbourhood-based diffusion algorithm for personalized recommendation |
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A new neighbourhood-based diffusion algorithm for personalized recommendation |
| abstract |
Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user. To improve the accuracy and diversity of recommender systems, we proposed a neighbourhood-based diffusion recommendation algorithm (NBD) that distributes the resources among objects using the rating scores of the objects based on the likings of the target user neighbours. Specifically, the Adamic–Adar similarity index is used to calculate the similarity between the target user and other users to select the top K similar neighbours to begin the diffusion process. In this approach, greater significance is put on common neighbours with fewer neighbour nodes. This is to reduce the effect of popular objects. At the end of the diffusion process, a modified redistribution algorithm using the sigmoid function is explored to finally redistribute the resources to the objects. This is to ensure that the objects recommended are personalized to target users. The evaluation has been conducted through experiments using four real-world datasets (Friendfeed, Epinions, MovieLens-100 K, and Netflix). The experiment results show that the performance of our proposed NBD algorithm is better in terms of accuracy when compared with the state-of-the-art algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| score |
7.400449 |