Point-of-interest Recommendation using Deep Semantic Model

Under the current paradigm, POI (Point-of-interest) recommendation tasks are mainly focused on representation learning. Therefore, the quality of the trajectory embeddings plays a key role in prediction. Existing methods mainly focus on learning the contextual information of check-in sequences, however, the contextual information is not the only medium for establishing state transitions between check-ins. Semantic information has been proven a powerful medium that can be learned with semantic data and injected into the source sequential embeddings for better prediction in other recommendation tasks. Unfortunately, for POI recommendation tasks, most of the key elements in the trajectory are discrete and there is no explicit semantic information. Therefore we argue that the deep and rich semantic information hidden in trajectories has not been fully exploited currently and how learning the deep semantic information from discrete trajectory data to improve the quality of the trajectory embedding is the key to further improving recommendation performance. We propose DSMR, a deep semantic recommender model for the next POI recommendation task to mitigate the issue. Specifically, we use prompt engineering to carry out continuous semantic modeling of discrete trajectory data and use the pre-trained language model to extract its implicit deep semantic information to establish causal transfer constraints between check-ins through the medium of semantics. Meanwhile, we propose a new position encoding function, temporal interval encoding, to avoid the neglect of temporal information of the check-ins sequence under the self-attention mechanism. Extensive experiments on two real-world datasets demonstrate the superior performance of our model to state-of-the-art approaches.