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

Published: 29 January 2020 Publication History

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
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 ACM 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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Association for Computing Machinery

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Publication History

Published: 29 January 2020
Accepted: 01 October 2019
Revised: 01 September 2019
Received: 01 June 2019
Published in TOIT Volume 20, Issue 1

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Author Tags

  1. SSL-SVD
  2. SVD++
  3. Sparse trust
  4. Transductive Support Vector Machine
  5. recommendation system

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  • Research-article
  • Research
  • Refereed

Funding Sources

  • Natural Science Foundation of Hunan Province
  • Major Scientific Research Fund of Innovation Group projects of Guizhou Province Office of Education
  • State Key Development Program of China
  • National Science Foundation of China

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  • (2025)Knowledge-aware recommendation based on hypergraph representation learning and transformer model optimizationApplied Intelligence10.1007/s10489-025-06257-z55:5Online publication date: 16-Jan-2025
  • (2024)An interpretable approach using hybrid graph networks and explainable AI for intelligent diagnosis recommendations in chronic disease careBiomedical Signal Processing and Control10.1016/j.bspc.2023.10591391(105913)Online publication date: May-2024
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  • (2023)GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender SystemAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0811-6_5(78-92)Online publication date: 20-Oct-2023
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