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On using category experts for improving the performance and accuracy in recommender systems

Authors:

Won-Seok Hwang,

Ho-Jong Lee,

Sang-Wook Kim,

Minsoo LeeAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 2355 - 2358

https://doi.org/10.1145/2396761.2398639

Published: 29 October 2012 Publication History

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Abstract

A variety of recommendation methods have been proposed to satisfy the performance and accuracy; however, it is fairly difficult to satisfy both of them because there is a trade-off between them. In this paper, we introduce the notion of category experts and propose the recommendation method by exploiting the ratings of category experts instead of those of the users similar to a target user. We also extend the method that uses both the category preference of a target user and his/her similarity to category experts. We show that our method significantly outperforms the existing methods in terms of performance and accuracy through extensive experiments with real-world data.

References

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Cited By

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Shu HHuang J(2023)Multi-task feature and structure learning for user-preference based knowledge-aware recommendationNeurocomputing10.1016/j.neucom.2023.02.023532(43-55)Online publication date: May-2023
https://doi.org/10.1016/j.neucom.2023.02.023
Yin FJi MWang YYao ZFeng XLi S(2022)Enhanced graph recommendation with heterogeneous auxiliary informationComplex & Intelligent Systems10.1007/s40747-022-00645-58:3(2311-2324)Online publication date: 24-Jan-2022
https://doi.org/10.1007/s40747-022-00645-5
Su BZheng KWang W(2021)A GitHub Project Recommendation Model Based on Self-Attention SequenceProceedings of the 2021 3rd International Conference on Big Data Engineering10.1145/3468920.3468936(110-116)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3468920.3468936
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Index Terms

On using category experts for improving the performance and accuracy in recommender systems
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

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Association for Computing Machinery

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Published: 29 October 2012

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Shu HHuang J(2023)Multi-task feature and structure learning for user-preference based knowledge-aware recommendationNeurocomputing10.1016/j.neucom.2023.02.023532(43-55)Online publication date: May-2023
https://doi.org/10.1016/j.neucom.2023.02.023
Yin FJi MWang YYao ZFeng XLi S(2022)Enhanced graph recommendation with heterogeneous auxiliary informationComplex & Intelligent Systems10.1007/s40747-022-00645-58:3(2311-2324)Online publication date: 24-Jan-2022
https://doi.org/10.1007/s40747-022-00645-5
Su BZheng KWang W(2021)A GitHub Project Recommendation Model Based on Self-Attention SequenceProceedings of the 2021 3rd International Conference on Big Data Engineering10.1145/3468920.3468936(110-116)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3468920.3468936
Shu HHuang J(2021)User-Preference Based Knowledge Graph Feature and Structure Learning for Recommendation2021 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME51207.2021.9428363(1-6)Online publication date: 5-Jul-2021
https://doi.org/10.1109/ICME51207.2021.9428363
Feng WZhu QZhuang JYu S(2018)An expert recommendation algorithm based on Pearson correlation coefficient and FP-growthCluster Computing10.1007/s10586-017-1576-yOnline publication date: 3-Jan-2018
https://doi.org/10.1007/s10586-017-1576-y
Sun ZGuo GZhang JShin SShin DLencastre M(2017)Learning hierarchical category influence on both users and items for effective recommendationProceedings of the Symposium on Applied Computing10.1145/3019612.3019763(1679-1684)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3019612.3019763
E HWANG JSONG MBI QLIU Y(2016)Incremental weighted bipartite algorithm for large-scale recommendation systemsTURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES10.3906/elk-1307-9124(448-463)Online publication date: 2016
https://doi.org/10.3906/elk-1307-91
Ali IKim SKim DKim SLee SHanzo LIsmail R(2015)Group recommendationsProceedings of the 9th International Conference on Ubiquitous Information Management and Communication10.1145/2701126.2701208(1-6)Online publication date: 8-Jan-2015
https://dl.acm.org/doi/10.1145/2701126.2701208
Ali IKim SWainwright RCorchado JBechini AHong J(2015)An effective approach to group recommendation based on belief propagationProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695840(1148-1153)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2695840
Lee SKim SPark SSuen CAghdam AGuo MHong JNadimi E(2013)Recommendation in online shopping mallsProceedings of the 2013 Research in Adaptive and Convergent Systems10.1145/2513228.2513254(116-117)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1145/2513228.2513254
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Improving Accuracy of Recommender System by Item Clustering

Using past-prediction accuracy in recommender systems

Improving collaborative filtering-based recommender systems results using Pareto dominance

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