MatTrip: Multi-functional Attention-based Neural Network for Semantic Travel Route Recommendation

Abstract

Travel route recommendation aims to recommend a sequence of point of interests (POIs) for visitors based on their personal interests. Previous studies utilize user interest features and POI spatial information to provide travel route recommendation service. However, most of them fail to consider the implicit information in user traveling patterns, which is the key to improve recommendation quality. Additionally, few deep learning based travel route recommendation systems provide comprehensive trip planning functionalities, which is critical to improve the user experience. To alleviate these two problems, we propose a multi-functional attention-based neural network for route recommendation (named MatTrip). We first introduce an encoder-decoder structure with a novel dual bi-directional LSTM encoder as the sequence generation model to learn other users' traveling records and generates a semantic travel route based on user preference and geographical features of start/end POI. Next, multiple user-specific functionalities are supported in MatTrip by grid beam search. The functionalities include weather dependency, POI opening hours, restricted sequence length, mandatory POIs, and dynamic route revision. In addition, MatTrip adopts an online learning approach to learn from user deviation behaviors to improve recommendation performance. Experiments on two real-world datasets show that our model achieves a 20.98% improvement in performance, compared with state-of-arts.