TSCMF: Temporal and social collective matrix factorization model for recommender systems

In real-world recommender systems, user preferences are dynamic and typically change
over time. Capturing the temporal dynamics of user preferences is essential to design an efficient
personalized recommender system and has recently attracted significant attention. In
this paper, we consider user preferences change individually over time. Moreover, based on
the intuition that social influence can affect the users’ preferences in a recommender system,
we propose a Temporal and Social CollectiveMatrix Factorization model called TSCMF for
recommendation.We jointly factorize the users’ rating information and social trust information
in a collective matrix factorization framework by introducing a joint objective function.
We model user dynamics into this framework by learning a transition matrix of user preferences
between two successive time periods for each individual user. We present an efficient
optimization algorithm based on stochastic gradient descent for solving the objective function.
The experiments on a real-world dataset illustrate that the proposed model outperforms
the competitive methods.Moreover, the complexity analysis demonstrates that the proposed
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model can be scaled up to large datasets.