Matrix Factorization for Personalized Recommendation With Implicit Feedback and Temporal Information in Social Ecommerce Networks

Collaborative filtering with implicit feedback is regarded as one of the most challenging issues in social ecommerce networks. However, the scarcity of negative feedback and the impact of time makes collaborative filtering difficult to use. Most models assign a uniform weight to missing data, but this method is invalid in the real world and leads to a biased representation of user profiles. In social ecommerce networks, the popularity of an item is implicit social information that is dynamic and can affect the preferences of a user. In this paper, we propose a smart model named TimeMF to address the above issues by incorporating implicit feedback and temporal information into social ecommerce recommendation. The weighting scheme is based on the dynamic popularity of an item. Then, we present an objective function and adopt an optimization strategy to enhance the efficiency. The experimental results for a real-world dataset reveal that our model outperforms the baselines on several metrics.