AGREE: Attention-Based Tour Group Recommendation with Multi-modal Data

Abstract

Tour recommendation aims to design a sequence of Points of Interest (POIs) for a tourist that suits his/her preference. Most existing tour recommenders mainly focus on recommending a POI sequence to a single tourist but cannot be applied to the tour group, which is a common way to travel. Designing a tour group recommender is more challenging in aggregating group preference and tracking influence changes during a tour. Hence we propose a novel approach named AGREE (Attention-based Tour Group Recommendation), which leverages the attention mechanism, to adjust members’ influence dynamically. Specifically, our model aggregates group’s preference based on members’ history data in different modalities, utilizing attention sub-networks to focus on influential ones in each modality across a POI sequence. Then we adopt a bi-directional recurrent unit (Bi-GRU) to generate the POI sequence. Experimental results show that the proposed scheme outperforms benchmark methods on a real-world dataset.

This work was supported by the National Key R&D Program of China [2018YFB1004703]; the National Natural Science Foundation of China [61872238, 61672353]; the Shanghai Science and Technology Fund [17510740200]; the Huawei Innovation Research Program [HO2018085286]; and the State Key Laboratory of Air Traffic Management System and Technology [SKLATM20180X].