Learning Neighbor User Intention on User-Item Interaction Graphs for Better Sequential Recommendation

The task of sequential recommendation aims to predict a user’s preference by analyzing the user’s historical behaviours. Existing methods model item transitions through leveraging sequential patterns. However, they mainly consider the target user’s behaviours and dynamic characteristics, while often ignoring high-order collaborative connections when modelling user preferences. Some recent works try to use graph-based methods to introduce high-order collaborative signals for sequential recommendation. However, these methods are flawed by two problems: the sequential patterns cannot be effectively mined and their way of introducing high-order collaborative signals is not suitable for sequential recommendation. To address these problems, we propose to fully exploit sequence features and model high-order collaborative signals for sequential recommendation. We propose a N eighbor user I ntention-based S equential Rec ommender (NISRec), which utilizes the intentions of high-order connected neighbor users as high-order collaborative signals in order to improve recommendation performance for the target user. The NISRec contains two main modules: the neighbor user intention embedding module (NIE) and the fusion module. The NIE module describes both the long-term and short-term intentions of neighbor users and aggregates them separately. The fusion module uses these two types of aggregated intentions to model high-order collaborative signals in both the embedding process and user preference modelling phase for recommendations of the target user. Experimental results show that our new approach outperforms the state-of-the-art methods on both sparse and dense datasets. Extensive studies further show the effectiveness of the diverse neighbor intentions introduced by the NISRec.