
The author proposes Continuous Input Embedding Size Search (CIESS) as a novel RL-based method to optimize embedding sizes for memory-efficient recommendations.


coremsg

continuous-input-embedding-size-search-for-recommender-systems-a-novel-approach-for-memory-efficient-recommendations


Continuous Input Embedding Size Search For Recommender Systems: A Novel Approach for Memory-Efficient Recommendations


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Latent factor models in recommender systems can benefit from continuous input embedding size search to optimize memory efficiency and recommendation performance.


continuous-input-embedding-size-search-for-recommender-systems-a-novel-rl-based-method-for-memory-efficient-recommendation


Continuous Input Embedding Size Search For Recommender Systems: A Novel RL-Based Method for Memory-Efficient Recommendation
