UniWalk: Explainable and Accurate Recommendation for Rating and Network Data
This addresses the need for persuasive and effective recommender systems in online services by integrating heterogeneous data, though it appears incremental in combining existing graph-based methods.
The paper tackles the problem of leveraging both social network data and observed ratings for accurate and explainable recommendations, achieving state-of-the-art accuracy and best explainability in experiments.
How can we leverage social network data and observed ratings to correctly recommend proper items and provide a persuasive explanation for the recommendations? Many online services provide social networks among users, and it is crucial to utilize social information since recommendation by a friend is more likely to grab attention than the one from a random user. Also, explaining why items are recommended is very important in encouraging the users' actions such as actual purchases. Exploiting both ratings and social graph for recommendation, however, is not trivial because of the heterogeneity of the data. In this paper, we propose UniWalk, an explainable and accurate recommender system that exploits both social network and rating data. UniWalk combines both data into a unified graph, learns latent features of users and items, and recommends items to each user through the features. Importantly, it explains why items are recommended together with the recommendation results. Extensive experiments show that UniWalk provides the best explainability and achieves the state-of-the-art-accuracy.