Learning User Preference from Heterogeneous Information for Store-type Recommendation

Online stores are capable analyzing user preference from click logs and transaction records, while retailers of physical stores still lack effective methods to understand user preference. Traditional ways are predominantly field surveys, which are not effective as they need labor-intensive survey thus limit to small populations. As mobile devices and social media are becoming more and more pervasive, user-generated heterogeneous information (e.g., check-in activities and textual reviews) from these platforms are providing rich information to in-depth understand user preference. In this paper, we present a recommendation model for physical stores by learning user's preference from user-generated heterogeneous information. Specifically, the proposed model consists of two phases: 1) offline modeling multi-relation among users, stores and aspects; 2) online graph-based recommendation. The offline modeling phase is designed to learn two kinds of relations: User-Store relation and Store-Aspect relation, while the online recommendation phase automatically produces top-k recommended stores based on the learnt relations with a graph-based model. To demonstrate the utility of our proposed model, we have performed a comprehensive experimental evaluation on two real-world datasets, which are collected by more than 120,000 users during 12 months. Experimental results show our method outperforms all baselines significantly in terms of recommendation effectiveness.