Abstract
In online social networks, social recommendation incorporates social relationships to solve data sparsity problem. However, existing studies ignore temporal dynamics of social relationships. To improve recommendation accuracy, this paper proposes an improved social recommendation model, which takes temporal dynamics of social relationships into consideration. It infers users’ social influence from their interaction records, and weights social influence differently according to the time distance. It also employs matrix factorization technique to fuse temporal dynamics of social relationships and user preference features together, demonstrating time-dependent change of user preference. An empirical analysis on Epinions dataset demonstrates that our approach performs better on improving predicted accuracy compared with current social recommendation models.
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