Automated Embedding Size Search in Deep Recommender Systems

SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July, 2020, pp. 2307-2316, 2020.

Cited by: 0|Bibtex|Views142|DOI:https://doi.org/10.1145/3397271.3401436
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Other Links: dl.acm.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

Deep recommender systems have achieved promising performance on real-world recommendation tasks. They typically represent users and items in a low-dimensional embedding space and then feed the embeddings into the following deep network structures for prediction. Traditional deep recommender models often adopt uniform and fixed embedding s...More

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