research-article

A General Framework for Implicit and Explicit Social Recommendation

Authors:

Shou-de LinAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 30, Issue 12

Pages 2228 - 2241

https://doi.org/10.1109/TKDE.2018.2821174

Published: 01 December 2018 Publication History

Abstract

Research of social recommendation aims at exploiting social information to improve the quality of a recommender system. It can be further divided into two classes. Explicit social recommendation assumes the existence of not only the users’ ratings on items, but also the explicit social connections between users. Implicit social recommendation assumes the availability of only the ratings but not the social connections between users, and attempts to infer implicit social connections between users with the goal to boost recommendation accuracy. This paper proposes a unified framework that is applicable to both explicit and implicit social recommendation. We propose an optimization framework to learn the degree of social correlation and rating prediction jointly, so these two tasks can mutually boost the performance of each other. Furthermore, a well-known challenge for implicit social recommendation is that it takes quadratic time to learn the strength of pairwise connections. This paper further proposes several practical tricks to reduce the complexity of our model to be linear to the observed ratings. The experiments show that the proposed model, with only two parameters, can significantly outperform the state-of-the-art solutions for both explicit and implicit social recommender systems.

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Cited By

Zhu HXiong FChen HXiong XWang L(2024)Incorporating a Triple Graph Neural Network with Multiple Implicit Feedback for Social RecommendationACM Transactions on the Web10.1145/358051718:2(1-26)Online publication date: 8-Jan-2024
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Yuan KLiu GWu JXiong H(2023)Semantic and Structural View Fusion Modeling for Social RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323097235:11(11872-11884)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3230972
Gao YHuang ZHuang ZHuang LKuang YYang X(2023)Multi-scale broad collaborative filtering for personalized recommendationKnowledge-Based Systems10.1016/j.knosys.2023.110853278:COnline publication date: 25-Oct-2023
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A General Framework for Implicit and Explicit Social Recommendation
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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 30, Issue 12

Dec. 2018

222 pages

ISSN:1041-4347

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1041-4347 © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.

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IEEE Educational Activities Department

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Published: 01 December 2018

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Cited By

Zhu HXiong FChen HXiong XWang L(2024)Incorporating a Triple Graph Neural Network with Multiple Implicit Feedback for Social RecommendationACM Transactions on the Web10.1145/358051718:2(1-26)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3580517
Yuan KLiu GWu JXiong H(2023)Semantic and Structural View Fusion Modeling for Social RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323097235:11(11872-11884)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3230972
Gao YHuang ZHuang ZHuang LKuang YYang X(2023)Multi-scale broad collaborative filtering for personalized recommendationKnowledge-Based Systems10.1016/j.knosys.2023.110853278:COnline publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110853
Weng LZhang QLin ZWu LZhang J(2023)Integrating interactions between target users and opinion leaders for better recommendationsComputer Communications10.1016/j.comcom.2022.11.011198:C(98-107)Online publication date: 15-Jan-2023
https://dl.acm.org/doi/10.1016/j.comcom.2022.11.011
Bawack RBonhoure E(2023)Influencer is the New Recommender: insights for Theorising Social Recommender SystemsInformation Systems Frontiers10.1007/s10796-022-10262-925:1(183-197)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s10796-022-10262-9
Zhang ZJin YChen LBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)A Diary Study of Social Explanations for Recommendations in Daily LifeAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3537681(200-208)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3511047.3537681
Liu ZLuo XZhou M(2021)Symmetry-constrained Non-negative Matrix Factorization Approach for Highly-Accurate Community Detection2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)10.1109/CASE49439.2021.9551446(1521-1526)Online publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1109/CASE49439.2021.9551446
Do QLiu WFan JTao D(2020)Unveiling Hidden Implicit Similarities for Cross-Domain RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.292390433:1(302-315)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1109/TKDE.2019.2923904

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