KDRank: Knowledge-driven user-aware POI recommendation

Accurate user modeling is crucial for point-of-interest (POI) recommendation as it can significantly improve user satisfaction with recommended POIs and enrich user experience. However, existing methods typically rely on simple time-series models for user check-in sequences, which ignore similar information of global users and fail to capture the user-preference knowledge hidden in complex social networks. To address this issue, we propose a novel knowledge-driven and user-aware POI recommendation method called KDRank. First, we construct a knowledge graph containing users' personal attributes for POI recommendation, which can reflect users' historical check-in preferences. Second, we derive users' knowledge representations using a cross-embedding method, which facilitates feature interaction by sharing information between segments of knowledge representations to achieve a more precise representation of low-dimensional embedding. Third, we propose a knowledge aggregation module to combine users' knowledge and historical check-in features to achieve knowledge enhancement of check-in data. Furthermore, to enhance global user awareness of our model, we introduce an attention mechanism that focuses on the most similar and significant users in the global context. It allows KDRank to capture more personalized user preferences and increases the precision of POI recommendations. The effectiveness of the proposed method was evaluated on two real datasets, and the results indicated its ability to increase the POI recommendation accuracy. The code associated with this study is available at https://github.com/itshardtocode/KDRank. & COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

Accurate user modeling is crucial for improving user satisfaction with recommended POIs and enriching user experience in point-of-interest (POI) recommendation. However, existing methods lack the ability to capture user-preference knowledge hidden in complex social networks. To address this issue, we propose a novel knowledge-driven and user-aware POI recommendation method called KDRank, which constructs a knowledge graph and employs cross-embedding, knowledge aggregation, and attention mechanism to enhance the accuracy of POI recommendations. Experimental results on real datasets demonstrate the effectiveness of our proposed method.