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Multi-category item recommendation using neighborhood associations in trust networks

Authors:

Zhuo YangAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 403 - 404

https://doi.org/10.1145/2567948.2577326

Published: 07 April 2014 Publication History

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Abstract

This paper proposes a novel recommendation method called RecDI. In the multi-category item recommendation domain, RecDI is designed to combine user ratings with information involving user's direct and indirect neighborhood associations. Through relevant benchmarking experiments on two real-world datasets, we show that RecDI achieves better performance than other traditional recommendation methods, which demonstrates the effectiveness of RecDI.

References

[1]

J. Tang, H. Gao, H. Liu, and A. D. Sarma. eTrust: Understanding trust evolution in an online world. In KDD, pages 253--261, 2012.

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[2]

R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, pages 1257--1264, 2008.

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Peng CXia FNaseriparsa MOsborne F(2023)Knowledge Graphs: Opportunities and ChallengesArtificial Intelligence Review10.1007/s10462-023-10465-956:11(13071-13102)Online publication date: 3-Apr-2023
https://doi.org/10.1007/s10462-023-10465-9
Shi YJin LChen JZeng K(2019)Recommendation of Item Category Based on Random Walk Model2019 IEEE 4th International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA.2019.8713258(226-230)Online publication date: Mar-2019
https://doi.org/10.1109/ICBDA.2019.8713258
Liu HXia FChen ZAsabere NMa JHuang R(2017)TruCom: Exploiting Domain-Specific Trust Networks for Multicategory Item RecommendationIEEE Systems Journal10.1109/JSYST.2015.242719311:1(295-304)Online publication date: Mar-2017
https://doi.org/10.1109/JSYST.2015.2427193
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Index Terms

Multi-category item recommendation using neighborhood associations in trust networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 07 April 2014

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WWW '14

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IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Peng CXia FNaseriparsa MOsborne F(2023)Knowledge Graphs: Opportunities and ChallengesArtificial Intelligence Review10.1007/s10462-023-10465-956:11(13071-13102)Online publication date: 3-Apr-2023
https://doi.org/10.1007/s10462-023-10465-9
Shi YJin LChen JZeng K(2019)Recommendation of Item Category Based on Random Walk Model2019 IEEE 4th International Conference on Big Data Analytics (ICBDA)10.1109/ICBDA.2019.8713258(226-230)Online publication date: Mar-2019
https://doi.org/10.1109/ICBDA.2019.8713258
Liu HXia FChen ZAsabere NMa JHuang R(2017)TruCom: Exploiting Domain-Specific Trust Networks for Multicategory Item RecommendationIEEE Systems Journal10.1109/JSYST.2015.242719311:1(295-304)Online publication date: Mar-2017
https://doi.org/10.1109/JSYST.2015.2427193
Liu JZhang LSheng VLaili Y(2017)Social Recommendation Terms: Probabilistic Explanation OptimizationChallenges and Opportunity with Big Data10.1007/978-3-319-61994-1_15(155-167)Online publication date: 4-Aug-2017
https://doi.org/10.1007/978-3-319-61994-1_15

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Recommendations

Using a trust network to improve top-N recommendation

Social Trust-Aware Recommendation System: A T-Index Approach

Developing trust networks based on user tagging information for recommendation making

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