Abstract
The problem of sequential recommendation aims at predicting the most likely item that user will interact based on historical interaction sequence. However, the previous methods only consider the proximity correlations among items and neglect the internal correlations when exploiting auxiliary information, and thus are insufficient to obtain accurate item embedding. Inspired by the success of transformer in NLP, we propose a novel Knowledge Graph Transformer for Sequential Recommendation, KGT-SR for brevity. The main idea of KGT-SR is to extract the rich semantic information of items by utilizing knowledge graph and feed the fused position and item information into the transformer to well learn item representation. KGT-SR consists of embedding layer, knowledge extraction layer and prediction layer. Extensive experiments results on three real world recommendation scenarios show that KGT-SR not only outperforms state-of-the-art sequential recommendation methods but also alleviates the problem of data sparsity.
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Acknowledgement
This work was supported by the Natural Science Foundation of Heilongjiang Province of China[grant numbers LH2022F045].
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Zhu, J., Cui, Y., Zhang, Z., Xi, H. (2023). Knowledge Graph Transformer for Sequential Recommendation. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14259. Springer, Cham. https://doi.org/10.1007/978-3-031-44223-0_37
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