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Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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
doi:10.24963/ijcai.2020/375
dblp:conf/ijcai/ReyMP20
fatcat:bvsodjpkvnar3dgdqbcs7lrq7e