TCMF: Trust-Based Context-Aware Matrix Factorization for Collaborative Filtering

Trust-aware recommender system (TARS) can provide more relevant recommendation and more accurate rating predictions than the traditional recommender system by taking the trust network into consideration. However, most of the trust-aware collaborative filtering approaches do not consider the influenc...

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Bibliographic Details
Main Authors: Jiyun Li, Caiqi Sun, Juntao Lv
Format: Conference Proceeding
Language:English
Subjects:
Collaboration
collaborative filtering
Context
Context modeling
context-aware
Filtering
matrix factorization
Predictive models
recommender system
Training
trust network
trust-based
Vectors
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Summary:Trust-aware recommender system (TARS) can provide more relevant recommendation and more accurate rating predictions than the traditional recommender system by taking the trust network into consideration. However, most of the trust-aware collaborative filtering approaches do not consider the influence of contextual information on rating prediction. To the opposite, context-aware matrix factorization approaches as we know do not take trust information into consideration. In this paper, we propose two Trust-based Context-aware Matrix Factorization (TCMF) approaches to fully capture the influence of trust information and contextual information on ratings. We integrate both trust information and contextual information into the baseline predictors (user bias and item bias) and user-item-context-trust interaction. Evaluations based on a real dataset and three semi-synthetic datasets demonstrate that our approaches can improve the accuracy of the trust-aware collaborative filtering and the context-aware matrix factorization models by at least 10.2% in terms of MAE.
ISSN:1082-3409
2375-0197
DOI:10.1109/ICTAI.2014.126