  Recommender systems can automatically recommend users with items that they
probably like. The goal of them is to model the user-item interaction by
effectively representing the users and items. Existing methods have primarily
learned the user's preferences and item's features with vectorized embeddings,
and modeled the user's general preferences to items by the interaction of them.
In fact, users have their specific preferences to item attributes and different
preferences are usually related. Therefore, exploring the fine-grained
preferences as well as modeling the relationships among user's different
preferences could improve the recommendation performance. Toward this end, we
propose a dual preference distribution learning framework (DUPLE), which aims
to jointly learn a general preference distribution and a specific preference
distribution for a given user, where the former corresponds to the user's
general preference to items and the latter refers to the user's specific
preference to item attributes. Notably, the mean vector of each Gaussian
distribution can capture the user's preferences, and the covariance matrix can
learn their relationship. Moreover, we can summarize a preferred attribute
profile for each user, depicting his/her preferred item attributes. We then can
provide the explanation for each recommended item by checking the overlap
between its attributes and the user's preferred attribute profile. Extensive
quantitative and qualitative experiments on six public datasets demonstrate the
effectiveness and explainability of the DUPLE method.
