As a fundamental yet significant process in personalized recommendation,
candidate generation and suggestion effectively help users spot the most
suitable items for them. Consequently, identifying substitutable items that are
interchangeable opens up new opportunities to refine the quality of generated
candidates. When a user is browsing a specific type of product (e.g., a laptop)
to buy, the accurate recommendation of substitutes (e.g., better equipped
laptops) can offer the user more suitable options to choose from, thus
substantially increasing the chance of a successful purchase. However, existing
methods merely treat this problem as mining pairwise item relationships without
the consideration of users' personal preferences. Moreover, the substitutable
relationships are implicitly identified through the learned latent
representations of items, leading to uninterpretable recommendation results. In
this paper, we propose attribute-aware collaborative filtering (A2CF) to
perform substitute recommendation by addressing issues from both
personalization and interpretability perspectives. Instead of directly
modelling user-item interactions, we extract explicit and polarized item
attributes from user reviews with sentiment analysis, whereafter the
representations of attributes, users, and items are simultaneously learned.
Then, by treating attributes as the bridge between users and items, we can
thoroughly model the user-item preferences (i.e., personalization) and
item-item relationships (i.e., substitution) for recommendation. In addition,
A2CF is capable of generating intuitive interpretations by analyzing which
attributes a user currently cares the most and comparing the recommended
substitutes with her/his currently browsed items at an attribute level. The
recommendation effectiveness and interpretation quality of A2CF are
demonstrated via extensive experiments on three real datasets.

提出了一种基于用户评论属性的协同过滤算法，可以在保证个性化推荐的同时，提高推荐结果的可解释性，尤其是在涉及可替代物品的推荐时。实验证明，该算法在三个真实数据集上的推荐效果和解释质量均较好。