Feng, X;
Chen, C;
Li, D;
Zhao, M;
Hao, J;
Wang, J;
(2021)
CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation.
In:
International Conference on Information and Knowledge Management, Proceedings.
(pp. pp. 484-493).
ACM: Association for Computing Machinery: New York, NY, United States.
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Abstract
Practical recommender systems experience a cold-start problem when observed user-item interactions in the history are insufficient. Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of the model and thus allowing fast adaptation to a specific task from limited data examples. Though with significant performance improvement, it commonly suffers from two critical issues: the non-compatibility with mainstream industrial deployment and the heavy computational burdens, both due to the inner-loop gradient operation. These two issues make them hard to be applied in practical recommender systems. To enjoy the benefits of meta learning framework and mitigate these problems, we propose a recommendation framework called Contextual Modulation Meta Learning (CMML). CMML is composed of fully feed-forward operations so it is computationally efficient and completely compatible with the mainstream industrial deployment. CMML consists of three components, including a context encoder that can generate context embedding to represent a specific task, a hybrid context generator that aggregates specific user-item features with task-level context, and a contextual modulation network, which can modulate the recommendation model to adapt effectively. We validate our approach on both scenario-specific and user-specific cold-start setting on various real-world datasets, showing CMML can achieve comparable or even better performance with gradient based methods yet with higher computational efficiency and better interpretability.
| Type: | Proceedings paper |
|---|---|
| Title: | CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation |
| Event: | CIKM '21: 30th ACM International Conference on Information & Knowledge Management |
| ISBN-13: | 9781450384469 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3459637.3482241 |
| Publisher version: | https://doi.org/10.1145/3459637.3482241 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | cold-start problem, meta learning, recommender system |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10144652 |
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