Abstract
Sequence recommendation aims to model the dynamic preferences of users from their historical interactions and accurately predict the next item that the users may be interested. Sequence recommendation models based on graph neural networks (GNNs) have become popular in academic research recently with remarkable results. However, it is difficult for existing GNNs-based models to learn the rapidly changing patterns of the user interests. Therefore, this paper proposes a novel GNNs-based model with a graph attention network (GAT) for the sequence recommendation, named Interactive Graph Attention Network Sequence Recommendation, IGANSR in short. In particular, the proposed IGANSR model constructs the user attributes graph and item attributes graph respectively to acquire the dynamic characteristics of both users and items. In addition, the IGANSR model utilizes a multi-layer graph attention network to dynamically learn the higher-order features and the representations of new nodes. Afterward, the IGANSR model can aggregate various information of each user’s neighbors’ graph and capture the embedding of similar users. Lastly, the proposed IGANSR model combines the dynamic item representations with the user representations together and projected onto multiple scales for the augmented learning. Experimental results carried out on three public datasets demonstrate that the IGANSR model outperforms other existing recommendation models.
This work is supported by National Natural Science Foundation of China (Grant No. 61902116).
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Liu, Q., Chen, J., Zhang, S., Liu, C., Wu, X. (2023). Sequence Recommendation Based on Interactive Graph Attention Network. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13624. Springer, Cham. https://doi.org/10.1007/978-3-031-30108-7_25
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