RNN-Based Sequence-Aware Recommenders for Tourist Attractions

Abstract

Selecting appropriate tourist attractions to visit in real time is an important problem for travellers. Since recommenders proactively suggest items based on user preference, they are a promising solution for this problem. Travellers visit tourist attractions sequentially by considering multiple attributes at the same time. Therefore, it is desirable to consider this when developing recommenders for tourist attractions. Using GRU4REC, we proposed RNN-based sequence-aware recommenders (RNN-SARs) that use multiple sequence datasets for training the recommended model, named multi-RNN-SARs. We proposed two types of multi-RNN-SARs—concatenate-RNN-SARs and parallel-RNN-SARs. In order to evaluate multi-RNN-SARs, we compared hit rate (HR) and mean reciprocal rank (MRR) of the item-based collaborative filtering recommender (item-CFR), RNN-SAR with the single-sequence dataset (basic-RNN-SAR), multi-RNN-SARs and the state-of-the-art SARs using a real-world travel dataset. Our research shows that multi-RNN-SARs have significantly higher performances compared to item-CFR. Not all multi-RNN-SARs outperform basic-RNN-SAR but the best multi-RNN-SAR achieves comparable performance to that of the state-of-the-art algorithms. These results highlight the importance of using multiple sequence datasets in RNN-SARs and the importance of choosing appropriate sequence datasets and learning methods for implementing multi-RNN-SARs in practice.