Abstract
Recommender systems generate personalized recommendations for users based on their historical data. However, if some users have few interactions in the training data, i.e., few-shot users, recommendations for them will be inaccurate. In this paper, we propose a setwise attentional neural similarity method (SANS) for the few-shot recommendation problem. Unlike general recommendation algorithms, we eliminate direct representations of few-shot users. First, a neural similarity method is proposed to effectively estimate the correlation between items. Then, we propose a setwise attention mechanism to obtain recommendation scores by aggregating the correlations between a candidate item and items in a candidate user’s historical interactions. To facilitate model training in the few-shot scenario, training samples are generated by episode sampling, and each training sample is assigned with an adaptive weight to emphasize the importance of few-shot users. We simulate the few-shot recommendation problem on three real-world datasets and extensive results show that SANS can outperform the state-of-the-art recommendation algorithms in few-shot recommendation.
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Acknowledgement
This work was supported by National Key Research and Development Project (No. 2018YFC0832303); National Natural Science Foundation of China (NSFC) under the Grants nos. 61932007 and 61902075.
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Zhang, Z., Lu, T., Li, D., Zhang, P., Gu, H., Gu, N. (2021). SANS: Setwise Attentional Neural Similarity Method for Few-Shot Recommendation. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_5
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