Abstract
Existing recommendation algorithms suffer from cold-start issues as it is challenging to learn accurate representations of cold-start users and items. In this paper, we formulate learning the representations of cold-start users and items as a few-shot learning task, and address it by training a representation function to predict the target user (item) embeddings based on limited training instances. Specifically, we propose a novel attention-based encoder serving as the neural function, with which the K training instances of a user (item) are viewed as the interactive context information to be further encoded and aggregated. Experiments show that our proposed method significantly outperforms existing baselines in predicting the representations of the cold-start users and items, and improves several downstream tasks where the embeddings of users and items are used.
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Notes
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When training \(g_\phi \), in MovieLens-1M, the items in \(D_T\) interact with more than 30 users, and this results 2819 items in \(D_T\) and 887 items in \(D_N\). In Pinterest, the items in \(D_T\) interact with more than 30 users, and this results 8544 items in \(D_T\) and 1372 items in \(D_N\).
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Acknowledgments
This work is supported by National Key R&D Program of China (No. 2018YFB1004401) and NSFC (No. 61532021, 61772537, 61772536, 61702522).
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Hao, B., Zhang, J., Li, C., Chen, H. (2020). Few-Shot Representation Learning for Cold-Start Users and Items. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_27
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