Abstract
With the increasing number of commodities in our daily life, the recommender system plays a more and more important role in selecting items of users’ interests. For the next basket recommendation task, in this work, we propose the first end-to-end correlation-aware model to predict the next basket considering intra-basket correlations using graph attention networks. Specifically, items and correlations between items are viewed as nodes and edges in a graph, respectively. By estimating and aggregating the intra-basket correlations using the attention layer of the self-attention model, the recommendation can be conducted at the basket level, instead of at the item level. We conduct comprehensive experiments on a real-world retailing dataset to show the improvement from state-of-the-art baselines using our proposed method.
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Notes
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Results of POP, triple2vec and DREAM are from [5].
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Zhang, Y., Luo, L., Zhang, J., Lu, Q., Wang, Y., Wang, Z. (2020). Correlation-Aware Next Basket Recommendation Using Graph Attention Networks. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_85
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DOI: https://doi.org/10.1007/978-3-030-63820-7_85
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