The authors propose a 'Performance Law' for sequential recommendation models that predicts HR and NDCG from model layers, embedding dimension, and number of tokens divided by Approximate Entropy, then uses the fitted formula to search for optimal configurations.
Compressed interaction graph based framework for multi-behavior recommendation
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Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy
The authors propose a 'Performance Law' for sequential recommendation models that predicts HR and NDCG from model layers, embedding dimension, and number of tokens divided by Approximate Entropy, then uses the fitted formula to search for optimal configurations.