Learning intermediary category labels for personal recommendation

Abstract

In many recommender systems, category information has been used as additional features for recommender for quite some time, whose application has tended to be understand relationships between products in order to surface recommendations that are relevant to a given context. Nevertheless, the categories as intermediary are labels for not only attributes of products but also preference characteristics of people, is ignored. Here we propose a framework to learn the intermediary role of categories acting as a bridge between users and items. The framework includes two parts. Firstly, we collect the intermediary factors that category labels affect attributes of items and user preferences respectively. Secondly, we integrate the category medium of assemble item attributes and user preferences to online recommender systems to help users discover similar or complementary products. We evaluate our framework on the Amazon product catalog and demonstrate hierarchy categories can capture characteristics of users and items simultaneously.