# Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

arXiv: https://arxiv.org/abs/2412.00430 · HTML: https://mlsanity.com/paper/2412.00430/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2024.

## Built on by (most cited first, 9 of 9)

- [Mimic In-Context Learning for Multimodal Tasks](https://mlsanity.com/paper/2504.08851/) (2025)
- [SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment](https://mlsanity.com/paper/2509.03934/) (2025)
- [Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration](https://mlsanity.com/paper/2604.10150/) (2026)
- [Revisiting scalable sequential recommendation with Multi-Embedding Approach and Mixture-of-Experts](https://mlsanity.com/paper/2510.25285/) (2025)
- [IE as Cache: Information Extraction Enhanced Agentic Reasoning](https://mlsanity.com/paper/2604.14930/) (2026)
- [Prompting is not Enough: Exploring Knowledge Integration and Controllable Generation](https://mlsanity.com/paper/2505.19660/) (2025)
- [A Novel Mamba-based Sequential Recommendation Method](https://mlsanity.com/paper/2504.07398/) (2025)
- [Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling](https://mlsanity.com/paper/2508.14910/) (2025)
- [The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders](https://mlsanity.com/paper/2606.21911/) (2026)

## Abstract (arXiv)

Scaling Laws have emerged as a powerful framework for understanding how model performance evolves as they increase in size, providing valuable insights for optimizing computational resources. In the realm of Sequential Recommendation (SR), which is pivotal for predicting users' sequential preferences, these laws offer a lens through which to address the challenges posed by the scalability of SR models. However, the presence of structural and collaborative issues in recommender systems prevents the direct application of the Scaling Law (SL) in these systems. In response, we introduce the Performance Law for SR models, which aims to theoretically investigate and model the relationship between model performance and data quality. Specifically, we first fit the HR and NDCG metrics to transformer-based SR models. Subsequently, we propose Approximate Entropy (ApEn) to assess data quality, presenting a more nuanced approach compared to traditional data quantity metrics. Our method enables accurate predictions across various dataset scales and model sizes, demonstrating a strong correlation in large SR models and offering insights into achieving optimal performance for any given model configuration.