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Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation

Authors :
Liu Yi
Zhang Bo
Rao Jun
Leyu Lin
Qiu Zhijie
Ruobing Xie
Source :
IJCAI
Publication Year :
2020
Publisher :
International Joint Conferences on Artificial Intelligence Organization, 2020.

Abstract

Real-world integrated personalized recommendation systems usually deal with millions of heterogeneous items. It is extremely challenging to conduct full corpus retrieval with complicated models due to the tremendous computation costs. Hence, most large-scale recommendation systems consist of two modules: a multi-channel matching module to efficiently retrieve a small subset of candidates, and a ranking module for precise personalized recommendation. However, multi-channel matching usually suffers from cold-start problems when adding new channels or new data sources. To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels. In experiments, we conduct both offline and online evaluations with case studies on a real-world integrated recommendation system. The significant improvements confirm the effectiveness and robustness of ICAN, especially for cold-start channels. Currently, ICAN has been deployed on WeChat Top Stories used by millions of users. The source code can be obtained from https://github.com/zhijieqiu/ICAN.

Details

Database :
OpenAIRE
Journal :
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Accession number :
edsair.doi...........68b85af2f5fffa332dc784d909d7099f