KAUST DepartmentKing Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Online Publication Date2019-11-04
Print Publication Date2019
Permanent link to this recordhttp://hdl.handle.net/10754/660624
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AbstractInterpretability of recommender systems has caused increasing attention due to its promotion of the effectiveness and persuasiveness of recommendation decision, and thus user satisfaction. Most existing methods, such as Matrix Factorization (MF), tend to be black-box machine learning models that lack interpretability and do not provide a straightforward explanation for their outputs. In this paper, we focus on probabilistic factorization model and further assume the absence of any auxiliary information, such as item content or user review. We propose an influence mechanism to evaluate the importance of the users' historical data, so that the most related users and items can be selected to explain each predicted rating. The proposed method is thus called Influence-based Interpretable Recommendation model (In2Rec). To further enhance the recommendation accuracy, we address the important issue of missing not at random, i.e., missing ratings are not independent from the observed and other unobserved ratings, because users tend to only interact what they like. In2Rec models the generative process for both observed and missing data, and integrates the influence mechanism in a Bayesian graphical model. A learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to maximum a posteriori estimation for In2Rec. A series of experiments on four real-world datasets (Movielens 10M, Netflix, Epinions, and Yelp) have been conducted. By comparing with the state-of-the-art recommendation methods, the experimental results have shown that In2Rec can consistently benefit the recommendation system in both rating prediction and ranking estimation tasks, and friendly interpret the recommendation results with the aid of the proposed influence mechanism.
CitationLiu, H., Wen, J., Jing, L., Yu, J., Zhang, X., & Zhang, M. (2019). In2Rec. Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM ’19. doi:10.1145/3357384.3358017
SponsorsThis work was supported in part by the National Natural Science Foundation of China under Grant 61822601, 61773050, 61672311 and 61632004; the Beijing Natural Science Foundation under Grant Z180006; the Beijing Municipal Science & Technology Commission under Grant Z181100008918012.
Conference/Event name28th ACM International Conference on Information and Knowledge Management, CIKM 2019