Sequential-hierarchical attention network: Exploring the hierarchical intention feature in POI recommendation

Recommender system has attracted increasing attentions of many service providers, as it plays an important role in helping user filter irrelevant information. As an important application in daily life, point-of-interest (POI) recommendation system has become a powerful tool for assisting users make travel decisions, by modeling the impact of external factors on user behavior, such as time, geographical location, to predict future check-ins. However, the influence of intention, an important internal factor, on user check-in behavior has not been well explored. Existing research lacks methods for intention representing learning in POI recommendation, and has not explore the relationship between intention prediction and check-in behavior prediction. Motivated by this, this paper develops a novel sequential-hierarchical attention neural network based recommendation method (SH-Rec), which learns the hierarchy association of intention and sequential dependency of behavior and its interactions to improve user representation in POI recommendation. The main idea of the proposed SH-Rec is to describe user intentions from both hierarchical and sequential aspects using historical check-in sequence and side information, such as POI category attributes. Specifically, we design a novel sequential-hierarchical attention network to model the interaction of hierarchical intention features and sequential behavior features, by stacking several LSTM and self-attention layers. Besides, we model user’s behavior patterns by extracting sequential preference features using memory network. To utilize the contribution of intention learning in recommendation, we propose a weighted optimization function by employing multi-task learning strategy, to migrate knowledge from intention prediction to check-in prediction. Extensive experiments over three real-world datasets evaluate the better performance of the proposed model than the state-of-the-art methods in terms of various evaluation metrics. A series of ablation experiments and parameter experiments verify the better robustness and stability of the proposed model.