A Novelty-Seeking Based Dining Recommender System

The rapid growth of location-based services provide the potential to understand people's mobility pattern at an unprecedented level, which can also enable food-service industry to accurately predict consumer's dining behavior. In this paper, by leveraging users' historical dining pattern, socio-demographic characteristics and restaurants' attributes, we aim at generating the top-K restaurants for a user's next dining. Compared to previous studies in location prediction which mainly focus on regular mobility patterns, we present a novelty-seeking based dining recommender system, termed NDRS, in consideration of both exploration and exploitation. First, we apply a Conditional Random Field (CRF) with additional constraints to infer users' novelty-seeking statuses by considering both spatial-temporal-historical features and users' socio-demographic characteristics. On the one hand, when a user is predicted to be novelty-seeking, by incorporating the influence of restaurants' contextual factors such as price and service quality, we propose a context-aware collaborative filtering method to recommend restaurants she has never visited before. On the other hand, when a user is predicted to be not novelty-seeking, we then present a Hidden Markov Model (HMM) considering the temporal regularity to recommend the previously visited restaurants. To evaluate the performance of each component as well as the whole system, we conduct extensive experiments, with a large dataset we have collected covering the concerned dining related check-ins, users' demographics, and restaurants' attributes. The results reveal that our system is effective for dining recommendation.