Cross Domain Embedding Transfer for Cold-start Users in Click-through Rate Prediction
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Yujie Pan, Ya ZhangAuthors Info & Claims
Yujie Pan, Ya ZhangAuthors Info & ClaimsArticle No.: 82, Pages 1 - 6
Abstract
Click-through Rate (CTR) prediction is one of the core tasks in recommender systems. Despite many successful CTR models, they suffer from poor performance on cold-start users who have no interactions for ID embedding learning. One promising cross-domain solution is to map user interactions from another source domain to the target embedding space. However, existing methods only train mapping functions with individual information on the overlapping user subset, which causes gaps with other non-overlapping active users. We propose to explicitly bridge the gap with a Linearly Neighbor Assembly (LNA) module. LNA interprets a cold-start user as a linear combination of well learned active users with similar interests, thus generating more stable and comprehensive user embeddings even if the individual information is not sufficient. The full Individual Transfer with Neighbor Assembly (ITNA) network combines individual transfer and neighbor support adaptively to produce the final embeddings. Experiments on several cross-domain scenarios show the effectiveness and compatibility of our proposed method.
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Published In
December 2021
699 pages
ISBN:9781450385053
DOI:10.1145/3508546
Copyright © 2021 ACM.
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Published: 25 February 2022
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ACAI'21
ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2021
Sanya, China
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