Learning Optimal Social Dependency for Recommendation
This work addresses the problem of improving recommendation accuracy in social recommender systems for users by learning optimal social dependencies, representing an incremental advancement over existing methods.
The paper tackles the challenge of exploiting optimal social dependency between users for recommendation tasks by proposing a probabilistic relational matrix factorization (PRMF) method, which models user latent features with a matrix variate normal distribution and learns dependency via a row precision matrix, and experimental results show it outperforms state-of-the-art approaches in terms of RMSE and MAE.
Social recommender systems exploit users' social relationships to improve the recommendation accuracy. Intuitively, a user tends to trust different subsets of her social friends, regarding with different scenarios. Therefore, the main challenge of social recommendation is to exploit the optimal social dependency between users for a specific recommendation task. In this paper, we propose a novel recommendation method, named probabilistic relational matrix factorization (PRMF), which aims to learn the optimal social dependency between users to improve the recommendation accuracy, with or without users' social relationships. Specifically, in PRMF, the latent features of users are assumed to follow a matrix variate normal (MVN) distribution. The positive and negative dependency between users are modeled by the row precision matrix of the MVN distribution. Moreover, we have also proposed an efficient alternating algorithm to solve the optimization problem of PRMF. The experimental results on real datasets demonstrate that the proposed PRMF method outperforms state-of-the-art social recommendation approaches, in terms of root mean square error (RMSE) and mean absolute error (MAE).