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Project Title

Personalized Long and Short term Preference Learning for Next POI Recommendation


Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model both users' general taste and recent sequential behavior. Moreover, different users show different dependencies on the two parts. However, most existing methods learn the same dependencies for different users. Besides, the locations and categories of POIs contain different information about users' preference. However, current researchers always treat them as the same factors or believe that categories determined where to go. To this end, we propose a novel method named Personalized Long- and Short-term Preference Learning (PLSPL) to learn the specific preference for each user. Specially, we combine the long- and short-term preference via user-based attention mechanism to learn the personalized weights on different parts for different users. Besides, the context information such as the category and check-in time is also important to capture user preference. Therefore, in long-term module, we consider the contextual features of POIs in users' history records and leverage attention mechanism to capture users' preference. In the short-term module, we train two LSTM models for location- and category-based sequence, respectively.

Branch: CSE     Domain: Data Mining

Developed In: Java