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Self-adjusting hybrid recommenders based on social network analysis

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SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 1147 - 1148

https://doi.org/10.1145/2009916.2010092

Published: 24 July 2011 Publication History

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Abstract

Ensemble recommender systems successfully enhance recom-mendation accuracy by exploiting different sources of user prefe-rences, such as ratings and social contacts. In linear ensembles, the optimal weight of each recommender strategy is commonly tuned empirically, with limited guarantee that such weights are optimal afterwards. We propose a self-adjusting hybrid recommendation approach that alleviates the social cold start situation by weighting the recommender combination dynamically at recommendation time, based on social network analysis algorithms. We show empirical results where our approach outperforms the best static combination for different hybrid recommenders.

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Cited By

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Wu YHuang Wayne WYu’d LTiand Y(2023)Group Recommendation Based on Heterogeneous Graph Algorithm for EBSNsIEEE Access10.1109/ACCESS.2022.322459811(1854-1866)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3224598
Mukherjee NKarthik G(2020)Framework for Evaluation of Explainable Recommender SystemProceedings of International Conference on Computational Intelligence and Data Engineering10.1007/978-981-15-8767-2_11(123-131)Online publication date: 21-Dec-2020
https://doi.org/10.1007/978-981-15-8767-2_11
Lanza DChávez FFernandez FGarcia-Valdez MTrujillo LOlague G(2016)Profiting from Several Recommendation Algorithms Using a Scalable ApproachNEO 201510.1007/978-3-319-44003-3_14(357-375)Online publication date: 24-Aug-2016
https://doi.org/10.1007/978-3-319-44003-3_14
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Index Terms

Self-adjusting hybrid recommenders based on social network analysis
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2011

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SIGIR '11

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SIGIR

SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all

Wu YHuang Wayne WYu’d LTiand Y(2023)Group Recommendation Based on Heterogeneous Graph Algorithm for EBSNsIEEE Access10.1109/ACCESS.2022.322459811(1854-1866)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3224598
Mukherjee NKarthik G(2020)Framework for Evaluation of Explainable Recommender SystemProceedings of International Conference on Computational Intelligence and Data Engineering10.1007/978-981-15-8767-2_11(123-131)Online publication date: 21-Dec-2020
https://doi.org/10.1007/978-981-15-8767-2_11
Lanza DChávez FFernandez FGarcia-Valdez MTrujillo LOlague G(2016)Profiting from Several Recommendation Algorithms Using a Scalable ApproachNEO 201510.1007/978-3-319-44003-3_14(357-375)Online publication date: 24-Aug-2016
https://doi.org/10.1007/978-3-319-44003-3_14
Xia JWu FXiong ZQiu MXie C(2016)Modeling recommender systems via weighted bipartite networkConcurrency and Computation: Practice and Experience10.1002/cpe.389529:14Online publication date: 3-Aug-2016
https://doi.org/10.1002/cpe.3895
Lacerda ASantos RVeloso AZiviani N(2014)Improving daily deals recommendation using explore-then-exploit strategiesInformation Retrieval Journal10.1007/s10791-014-9249-418:2(95-122)Online publication date: 21-Dec-2014
https://doi.org/10.1007/s10791-014-9249-4
Zhang ZZeng DAbbasi APeng JZheng X(2013)A Random Walk Model for Item Recommendation in Social Tagging SystemsACM Transactions on Management Information Systems10.1145/24908604:2(1-24)Online publication date: 1-Aug-2013
https://dl.acm.org/doi/10.1145/2490860
Ziegler COprea AHornung TEggs HLausen G(2013)Recommending Software Apps in a B2B ContextProceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0310.1109/WI-IAT.2013.195(270-273)Online publication date: 17-Nov-2013
https://dl.acm.org/doi/10.1109/WI-IAT.2013.195
BellogíN ACantador ICastells P(2013)A comparative study of heterogeneous item recommendations in social systemsInformation Sciences: an International Journal10.1016/j.ins.2012.09.039221(142-169)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.09.039
Bellogin AMobasher BBurke RJannach DAdomavicius G(2011)Predicting performance in recommender systemsProceedings of the fifth ACM conference on Recommender systems10.1145/2043932.2044009(371-374)Online publication date: 23-Oct-2011
https://dl.acm.org/doi/10.1145/2043932.2044009

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An empirical comparison of social, collaborative filtering, and hybrid recommenders

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