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An Efficient Large Recommendation Model: Towards a Resource-Optimal Scaling Law
The pursuit of scaling up recommendation models confronts intrinsic tensions between expanding model capacity and preserving computational tractability.
Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems
Based on the co-training of the two towers, the MAIL presents an end-to-end method for recommender systems that shows an incremental performance improvement.
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