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As a fundamental yet significant process in personalized recommendation,candidate generation and suggestion effectively help users spot the mostsuitable items for them. Consequently, identifying substitutable items that areinterchangeable opens up new opportunities to refine the quality of generatedcandidates. When a user is browsing a specific type of product (e.g., a laptop)to buy, the accurate recommendation of substitutes (e.g., better equippedlaptops) can offer the user more suitable options to choose from, thussubstantially increasing the chance of a successful purchase. However, existingmethods merely treat this problem as mining pairwise item relationships withoutthe consideration of users' personal preferences. Moreover, the substitutablerelationships are implicitly identified through the learned latentrepresentations of items, leading to uninterpretable recommendation results. Inthis paper, we propose attribute-aware collaborative filtering (A2CF) toperform substitute recommendation by addressing issues from bothpersonalization and interpretability perspectives. Instead of directlymodelling user-item interactions, we extract explicit and polarized itemattributes from user reviews with sentiment analysis, whereafter therepresentations of attributes, users, and items are simultaneously learned.Then, by treating attributes as the bridge between users and items, we canthoroughly model the user-item preferences (i.e., personalization) anditem-item relationships (i.e., substitution) for recommendation. In addition,A2CF is capable of generating intuitive interpretations by analyzing whichattributes a user currently cares the most and comparing the recommendedsubstitutes with her/his currently browsed items at an attribute level. Therecommendation effectiveness and interpretation quality of A2CF aredemonstrated via extensive experiments on three real datasets.

Try This Instead: Personalized and Interpretable Substitute Recommendation