Improving the recommender algorithms with the detected communities in bipartite networks
Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, wh...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Peng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
7 |
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| Übergeordnetes Werk: |
Enthalten in: Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study - Dai, Jiamiao ELSEVIER, 2022, europhysics journal, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:471 ; year:2017 ; day:1 ; month:04 ; pages:147-153 ; extent:7 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.physa.2016.11.076 |
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| 520 | |a Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. | ||
| 520 | |a Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. | ||
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10.1016/j.physa.2016.11.076 doi GBV00000000000076A.pica (DE-627)ELV020274351 (ELSEVIER)S0378-4371(16)30882-2 DE-627 ger DE-627 rakwb eng 500 500 DE-600 610 VZ 44.91 bkl Zhang, Peng verfasserin aut Improving the recommender algorithms with the detected communities in bipartite networks 2017transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. Community structure Elsevier Bipartite network Elsevier Recommender system Elsevier Wang, Duo oth Xiao, Jinghua oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:471 year:2017 day:1 month:04 pages:147-153 extent:7 https://doi.org/10.1016/j.physa.2016.11.076 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 471 2017 1 0401 147-153 7 045F 500 |
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10.1016/j.physa.2016.11.076 doi GBV00000000000076A.pica (DE-627)ELV020274351 (ELSEVIER)S0378-4371(16)30882-2 DE-627 ger DE-627 rakwb eng 500 500 DE-600 610 VZ 44.91 bkl Zhang, Peng verfasserin aut Improving the recommender algorithms with the detected communities in bipartite networks 2017transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. Community structure Elsevier Bipartite network Elsevier Recommender system Elsevier Wang, Duo oth Xiao, Jinghua oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:471 year:2017 day:1 month:04 pages:147-153 extent:7 https://doi.org/10.1016/j.physa.2016.11.076 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 471 2017 1 0401 147-153 7 045F 500 |
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10.1016/j.physa.2016.11.076 doi GBV00000000000076A.pica (DE-627)ELV020274351 (ELSEVIER)S0378-4371(16)30882-2 DE-627 ger DE-627 rakwb eng 500 500 DE-600 610 VZ 44.91 bkl Zhang, Peng verfasserin aut Improving the recommender algorithms with the detected communities in bipartite networks 2017transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. Community structure Elsevier Bipartite network Elsevier Recommender system Elsevier Wang, Duo oth Xiao, Jinghua oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:471 year:2017 day:1 month:04 pages:147-153 extent:7 https://doi.org/10.1016/j.physa.2016.11.076 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 471 2017 1 0401 147-153 7 045F 500 |
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10.1016/j.physa.2016.11.076 doi GBV00000000000076A.pica (DE-627)ELV020274351 (ELSEVIER)S0378-4371(16)30882-2 DE-627 ger DE-627 rakwb eng 500 500 DE-600 610 VZ 44.91 bkl Zhang, Peng verfasserin aut Improving the recommender algorithms with the detected communities in bipartite networks 2017transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. Community structure Elsevier Bipartite network Elsevier Recommender system Elsevier Wang, Duo oth Xiao, Jinghua oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:471 year:2017 day:1 month:04 pages:147-153 extent:7 https://doi.org/10.1016/j.physa.2016.11.076 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 471 2017 1 0401 147-153 7 045F 500 |
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10.1016/j.physa.2016.11.076 doi GBV00000000000076A.pica (DE-627)ELV020274351 (ELSEVIER)S0378-4371(16)30882-2 DE-627 ger DE-627 rakwb eng 500 500 DE-600 610 VZ 44.91 bkl Zhang, Peng verfasserin aut Improving the recommender algorithms with the detected communities in bipartite networks 2017transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. Community structure Elsevier Bipartite network Elsevier Recommender system Elsevier Wang, Duo oth Xiao, Jinghua oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:471 year:2017 day:1 month:04 pages:147-153 extent:7 https://doi.org/10.1016/j.physa.2016.11.076 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 471 2017 1 0401 147-153 7 045F 500 |
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Improving the recommender algorithms with the detected communities in bipartite networks |
| abstract |
Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. |
| abstractGer |
Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. |
| abstract_unstemmed |
Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
Improving the recommender algorithms with the detected communities in bipartite networks |
| url |
https://doi.org/10.1016/j.physa.2016.11.076 |
| remote_bool |
true |
| author2 |
Wang, Duo Xiao, Jinghua |
| author2Str |
Wang, Duo Xiao, Jinghua |
| ppnlink |
ELV00892340X |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1016/j.physa.2016.11.076 |
| up_date |
2024-07-06T17:10:03.939Z |
| _version_ |
1803850407154286592 |
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|
| score |
7.3998404 |