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Psrec: social recommendation with pseudo ratings

Published: 27 September 2018 Publication History

Abstract

Data sparsity and cold start are two major problems of collaborative filtering based recommender systems. In many modern Internet applications, we have a social network over the users of recommender systems, from which social information can be utilized to improve the accuracy of recommendation. In this paper, we propose a novel trust-based matrix factorization model. Unlike most existing social recommender systems which use social information in the form of a regularizer on parameters of recommendation algorithms, we utilize the social information to densify the training data set by filling certain missing values (handle the data sparsity problem). In addition, by employing different pseudo rating generating criteria on cold start users and normal users, we can also partially solve the cold start problem effectively. Experiment results on real-world data sets demonstrated the superiority of our method over state-of-art approaches.

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Cited By

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  • (2023)Comparative Studies on Modeling Users’ Multifaceted Interest Correlation for Social RecommendationProceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)10.2991/978-94-6463-198-2_137(1317-1328)Online publication date: 26-Jul-2023
  • (2020)SSL-SVDACM Transactions on Internet Technology10.1145/336939020:1(1-20)Online publication date: 29-Jan-2020
  • (2020)Wasserstein Collaborative Filtering for Item Cold-start RecommendationProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394870(318-322)Online publication date: 7-Jul-2020
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    cover image ACM Conferences
    RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
    September 2018
    600 pages
    ISBN:9781450359016
    DOI:10.1145/3240323
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 27 September 2018

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    Author Tags

    1. matrix factorization
    2. recommender systems
    3. social network

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    RecSys '18
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    RecSys '18: Twelfth ACM Conference on Recommender Systems
    October 2, 2018
    British Columbia, Vancouver, Canada

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    RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    Cited By

    View all
    • (2023)Comparative Studies on Modeling Users’ Multifaceted Interest Correlation for Social RecommendationProceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)10.2991/978-94-6463-198-2_137(1317-1328)Online publication date: 26-Jul-2023
    • (2020)SSL-SVDACM Transactions on Internet Technology10.1145/336939020:1(1-20)Online publication date: 29-Jan-2020
    • (2020)Wasserstein Collaborative Filtering for Item Cold-start RecommendationProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394870(318-322)Online publication date: 7-Jul-2020
    • (2020)Modeling Users’ Multifaceted Interest Correlation for Social RecommendationAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47426-3_10(118-129)Online publication date: 6-May-2020
    • (2020)PMD: An Optimal Transportation-Based User Distance for Recommender SystemsAdvances in Information Retrieval10.1007/978-3-030-45442-5_34(272-280)Online publication date: 8-Apr-2020
    • (2019)Feature evolution based multi-task learning for collaborative filtering with social trustProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367580(3877-3883)Online publication date: 10-Aug-2019
    • (2019)Personalized Recommendation via Trust-Based DiffusionIEEE Access10.1109/ACCESS.2019.29285747(94195-94204)Online publication date: 2019

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