Abstract
Personalized prediction for users based on their historical behavior sequences is a challenging problem in the field of recommender systems. The development of recurrent neural networks enables systems to better process sequence information to capture users’ long-term preferences. While it cannot effectively utilize both long-term and short-term preferences. In this paper, we propose a novel double-layer attention mechanism mode, which not only increases the weight of short-term preferences in the model, but also assigns separate weights to users’ recent behaviors, and then effectively preventing the prediction bias caused by mis-click. The proposed model is experimented on MovieLens, Amazon video game and Amazon digital music datasets, and the results show that our model achieves the best performance in all datasets.
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Acknowledgements
This work is supported by “Tianjin Project + Team” Key Training Project under Grant No. XC202022.
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Li, W., Yang, P., Wang, X., Xiao, Y. (2022). Sequence Recommendation Model with Double-Layer Attention Net. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13427. Springer, Cham. https://doi.org/10.1007/978-3-031-12426-6_7
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