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SSL-SVD: Semi-supervised Learning--based Sparse Trust Recommendation

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Published:29 January 2020Publication History
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Abstract

Recommendation systems have been widely used in large e-commerce websites, but cold start and data sparsity seriously affect the accuracy of recommendation. To solve these problems, we propose SSL-SVD, which works to mine the sparse trust between users and improve the performance of the recommendation system. Specifically, we mine sparse trust relationships by decomposing trust impact into fine-grained factors and employing the Transductive Support Vector Machine algorithm to combine these factors. Then, we incorporate both social trust and sparse trust information into the SVD++ model, which can effectively utilize the explicit and implicit influence of trust for rating prediction in the recommendation system. Experiments show that our SSL-SVD increases the trust density degree of each dataset by more than 65% and improves the recommendation accuracy by up to 4.3%.

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      • Published in

        cover image ACM Transactions on Internet Technology
        ACM Transactions on Internet Technology  Volume 20, Issue 1
        Visions and Regular Papers
        February 2020
        135 pages
        ISSN:1533-5399
        EISSN:1557-6051
        DOI:10.1145/3381410
        • Editor:
        • Ling Liu
        Issue’s Table of Contents

        Copyright © 2020 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 29 January 2020
        • Accepted: 1 October 2019
        • Revised: 1 September 2019
        • Received: 1 June 2019
        Published in toit Volume 20, Issue 1

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