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Continual Learning Based on Task Masking for Multi-domain Recommendation

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Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2024)

Abstract

In recent years, Multi-Domain Recommendation has become a big challenge of recommendation systems, due to the large number of overlapped users and items between multiple domains of many platforms. Multi-Domain Recommendation focuses on capturing informative domain specific features from all domains to improve the corresponding accuracy. In this paper, we proposed a light model, inspired by technique used in Continual Learning selecting the important parameters of users preference for each domain that highly reduces the bias. Our model could also be applied on top of any existing latent model effectively making them usable in multi-domain recommendation settings. We call our architecture as CL4Rec.

T.-N.-L. Nguyen and C.-D. Vu—Contributed equally to this work.

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Correspondence to Chi-Dung Vu .

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Nguyen, TNL. et al. (2024). Continual Learning Based on Task Masking for Multi-domain Recommendation. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2024. Communications in Computer and Information Science, vol 2145. Springer, Singapore. https://doi.org/10.1007/978-981-97-5934-7_22

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  • DOI: https://doi.org/10.1007/978-981-97-5934-7_22

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  • Print ISBN: 978-981-97-5933-0

  • Online ISBN: 978-981-97-5934-7

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