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 Collective Matrix 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 model can be scaled up to large datasets.

中文翻译：

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TSCMF：推荐系统的时间和社会集体矩阵分解模型

在现实世界的推荐系统中，用户偏好是动态的，通常会随时间变化。捕获用户偏好的时间动态对于设计高效的个性化推荐系统至关重要，并且最近引起了极大的关注。在本文中，我们考虑用户偏好随时间单独变化。此外，基于社会影响可以影响推荐系统中用户偏好的直觉，我们提出了一种称为 TSCMF 的时间和社会集体矩阵分解模型进行推荐。我们通过引入联合目标函数，在集体矩阵分解框架中联合分解用户的评分信息和社会信任信息。我们通过学习每个用户在两个连续时间段之间的用户偏好转换矩阵，将用户动态建模到这个框架中。我们提出了一种基于随机梯度下降的高效优化算法来求解目标函数。在真实世界数据集上的实验表明，所提出的模型优于竞争方法。此外，复杂性分析表明，所提出的模型可以扩展到大型数据集。