TriRank: Review-aware Explainable Recommendation by Modeling Aspects
Most existing collaborative filtering techniques have focused on modeling the binary relation of users to items by ex-tracting from user ratings. Aside from users ’ ratings, their affiliated reviews often provide the rationale for their rat-ings and identify what aspects of the item they cared most about. We explore the rich evidence source of aspects in user reviews to improve top-N recommendation. By extracting aspects (i.e., the specific properties of items) from textual reviews, we enrich the user–item binary relation to a user– item–aspect ternary relation. We model the ternary relation as a heterogeneous tripartite graph, casting the recommen-dation task as one of vertex ranking. We devise a generic algorithm for ranking on tripartite graphs — TriRank — and specialize it for personalized recommendation. Experiments on two public review datasets show that it consistently out-performs state-of-the-art methods. Most importantly, Tri-Rank endows the recommender system with a higher degree of explainability and transparency by modeling aspects in reviews. It allows users to interact with the system through their aspect preferences, assisting users in making informed decisions.