Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences
Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences
Lu Zhang, Zhu Sun, Ziqing Wu, Jie Zhang, Yew Soon Ong, Xinghua Qu
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 3751-3757.
https://doi.org/10.24963/ijcai.2022/521
Existing studies on next point-of-interest (POI) recommendation mainly attempt to learn user preference from the past and current sequential behaviors. They, however, completely ignore the impact of future behaviors on the decision-making, thus hindering the quality of user preference learning. Intuitively, users' next POI visits may also be affected by their multi-step future behaviors, as users may often have activity planning in mind. To fill this gap, we propose a novel Context-aware Future Preference inference Recommender (CFPRec) to help infer user future preference in a self-ensembling manner. In particular, it delicately derives multi-step future preferences from the learned past preference thanks to the periodic property of users' daily check-ins, so as to implicitly mimic user’s activity planning before her next visit. The inferred future preferences are then seamlessly integrated with the current preference for more expressive user preference learning. Extensive experiments on three datasets demonstrate the superiority of CFPRec against state-of-the-arts.
Keywords:
Machine Learning: Recommender Systems
Humans and AI: Personalization and User Modeling