research-article

Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback

Authors:

Shu-Jian Huang,

Jia-Jun ChenAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 12, Issue 2

Article No.: 23, Pages 1 - 30

https://doi.org/10.1145/3127873

Published: 23 January 2018 Publication History

Abstract

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors-based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although social matrix factorization (Social MF) and topic matrix factorization (Topic MF) successfully exploit social relations and item reviews, respectively; both of them ignore some useful information. In this article, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model MR3 to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.

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Cited By

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Chang JYu FOuyang CYang HHe QYu L(2024)An interval-valued matrix factorization based trust-aware collaborative filtering algorithm for recommendation systemsInformation Sciences10.1016/j.ins.2024.121355(121355)Online publication date: Aug-2024
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Index Terms

Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 12, Issue 2

Survey Papers and Regular Papers

April 2018

376 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3178544

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

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Copyright © 2018 ACM.

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Association for Computing Machinery

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Publication History

Published: 23 January 2018

Accepted: 01 July 2017

Revised: 01 June 2017

Received: 01 May 2016

Published in TKDD Volume 12, Issue 2

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Natural Science Foundation of Jiangsu Province for Excellent Young Scholar
863 program
National Science Foundation of China

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Cited By

Qin WLuo XLi SZhou M(2024)Parallel Adaptive Stochastic Gradient Descent Algorithms for Latent Factor Analysis of High-Dimensional and Incomplete Industrial DataIEEE Transactions on Automation Science and Engineering10.1109/TASE.2023.326760921:3(2716-2729)Online publication date: Jul-2024
https://doi.org/10.1109/TASE.2023.3267609
Liu X(2024)Teaching Design Model of Media Courses Based on Artificial IntelligenceIEEE Access10.1109/ACCESS.2024.345052912(121242-121250)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3450529
Chang JYu FOuyang CYang HHe QYu L(2024)An interval-valued matrix factorization based trust-aware collaborative filtering algorithm for recommendation systemsInformation Sciences10.1016/j.ins.2024.121355(121355)Online publication date: Aug-2024
https://doi.org/10.1016/j.ins.2024.121355
Fan GZhang CChen JLi PLin YLeung V(2023)Improving Rating Prediction in Multi-criteria Recommender Systems via a Collective Factor ModelSSRN Electronic Journal10.2139/ssrn.4618243Online publication date: 2023
https://doi.org/10.2139/ssrn.4618243
Fan GZhang CChen JLi PLi YLeung V(2023)Improving Rating Prediction in Multi-Criteria Recommender Systems Via a Collective Factor ModelIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.3270910(1-11)Online publication date: 2023
https://doi.org/10.1109/TNSE.2023.3270910
Cai XGuo WZhao MCui ZChen J(2023)A Knowledge Graph-Based Many-Objective Model for Explainable Social RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.328357410:6(3021-3030)Online publication date: Dec-2023
https://doi.org/10.1109/TCSS.2023.3283574
Li ZJin DYuan K(2023)Attentional factorization machine with review-based user–item interaction for recommendationScientific Reports10.1038/s41598-023-40633-413:1Online publication date: 18-Aug-2023
https://doi.org/10.1038/s41598-023-40633-4
Huang NHu RWang XDing HHuang X(2023)Cross-platform sequential recommendation with sharing item-level relevance dataInformation Sciences10.1016/j.ins.2022.11.112621(265-286)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.11.112
Spiliotopoulos DMargaris DVassilakis C(2022)On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering DatasetsInformation10.3390/info1309042813:9(428)Online publication date: 11-Sep-2022
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Ma MRen PChen ZRen ZZhao LLiu PMa Jde Rijke M(2022)Mixed Information Flow for Cross-Domain Sequential RecommendationsACM Transactions on Knowledge Discovery from Data10.1145/348733116:4(1-32)Online publication date: 8-Jan-2022
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