LAUSR

Abstract Recommender system uses user-item historical interactions to portray user preferences. Due to the problem of data sparseness, auxiliary information is introduced to describe user preferences, such as user/item attribute information. However, some of these methods only consider user(item) attributes when modeling user preferences. Although another part of the methods interact with user attributes and item attributes, this interaction doesn’t consider the potential preference of a certain attribute of the user to a certain attribute of the item. This makes the model unable to capture fine-grained user preferences. In fact, capturing fine-grained user preferences in terms of attributes is very effective in improving the recommendation effect. Therefore, in this paper we propose a recommendation method to capture user fine-grained preferences through attribute information. Specifically, there are two elaborately designed modules, interactive preference module and attribute fine-grained preference module. The former uses user-item historical interactions to model user’s interaction preferences. In order to obtain the user’s fine-grained preferences, the latter interacts with each attribute of the user and all the attributes of the item and uses the attention mechanism to mine the user’s more fine-grained preferences on attributes. Extensive experiments on three publicly available datasets demonstrate the effectiveness of the proposed method.