TransTARec: Time-Adaptive Translating Embedding Model for Next POI Recommendation
This work addresses next POI recommendation for users by integrating time into embeddings, though it is incremental as it builds on existing translating methods.
The paper tackled the problem of next POI recommendation by incorporating temporal influence into translating embedding methods, which had been neglected, and proposed TransTARec to unify temporal, sequential, and user preference factors, achieving improved performance as confirmed by experiments on real-world datasets.
The rapid growth of location acquisition technologies makes Point-of-Interest(POI) recommendation possible due to redundant user check-in records. In this paper, we focus on next POI recommendation in which next POI is based on previous POI. We observe that time plays an important role in next POI recommendation but is neglected in the recent proposed translating embedding methods. To tackle this shortage, we propose a time-adaptive translating embedding model (TransTARec) for next POI recommendation that naturally incorporates temporal influence, sequential dynamics, and user preference within a single component. Methodologically, we treat a (previous timestamp, user, next timestamp) triplet as a union translation vector and develop a neural-based fusion operation to fuse user preference and temporal influence. The superiority of TransTARec, which is confirmed by extensive experiments on real-world datasets, comes from not only the introduction of temporal influence but also the direct unification with user preference and sequential dynamics.