Scaling New Frontiers: Insights into Large Recommendation Models (2412.00714v1)

Abstract: Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate increasingly large embedding tables, scaling up to tens of terabytes for industrial use. However, the expansion of network parameters in traditional recommendation models has plateaued at tens of millions, limiting further benefits from increased embedding parameters. Inspired by the success of LLMs, a new approach has emerged that scales network parameters using innovative structures, enabling continued performance improvements. A significant development in this area is Meta's generative recommendation model HSTU, which illustrates the scaling laws of recommendation systems by expanding parameters to thousands of billions. This new paradigm has achieved substantial performance gains in online experiments. In this paper, we aim to enhance the understanding of scaling laws by conducting comprehensive evaluations of large recommendation models. Firstly, we investigate the scaling laws across different backbone architectures of the large recommendation models. Secondly, we conduct comprehensive ablation studies to explore the origins of these scaling laws. We then further assess the performance of HSTU, as the representative of large recommendation models, on complex user behavior modeling tasks to evaluate its applicability. Notably, we also analyze its effectiveness in ranking tasks for the first time. Finally, we offer insights into future directions for large recommendation models. Supplementary materials for our research are available on GitHub at https://github.com/USTC-StarTeam/Large-Recommendation-Models.

• The paper establishes scaling laws in large recommendation models, validated through extensive experiments with the HSTU architecture.
• It compares various transformer-based designs and uses ablation studies to highlight relative attention bias as a critical factor for enhanced HR and NDCG metrics.
• The study demonstrates that leveraging multi-behavior data and optimized embedding sizes significantly boosts ranking accuracy in complex user scenarios.

Evaluation and Insights into Large Recommendation Models

The paper "Scaling New Frontiers: Insights into Large Recommendation Models" explores the burgeoning domain of large recommendation models by examining their scalability and performance, particularly in light of parallels drawn from advancements in LLMs. In recent years, recommendation systems have become increasingly sophisticated, aiming to efficiently filter and retrieve relevant data. The advent of large-scale models, featuring tens of terabytes in embedding tables and thousands of billions of network parameters, signals a significant shift in the capabilities of recommendation systems.

Scaling Laws in Large Recommendation Models

The central thesis of this paper revolves around the exploration of scaling laws applied to large recommendation models, particularly through the lens of Meta's generative recommendation model, HSTU. The authors conduct an in-depth examination to validate the existence of such scaling laws and evaluate the corresponding performance improvements evident in extensive online experiments.

This work distinguishes itself by focusing on several key areas:

1. Impact of Backbone Architectures: The paper systematically compares the scalability of different transformer-based architectures, including HSTU, Llama, GPT, and SASRec, against varying model sizes across three benchmark datasets (MovieLens-1M, MovieLens-20M, and Amazon Books). Notably, HSTU and Llama exhibit robust scalability with increased model depth.
2. Ablation Studies of HSTU: By dissecting key components such as relative attention bias, SiLU functions for attention weights, and feature interaction, the paper identifies critical drivers of scalability within HSTU. Each component's influence on performance metrics—HR and NDCG—is rigorously examined, revealing relative attention bias as a vital contributor to scalability.
3. Performance Across Complex User Scenarios: The application potential of HSTU is further tested on complex behavior modeling, including scenarios with side information, multi-behavior, and multi-domain data. Evaluations show that while side information does not consistently enhance performance, it maintains model scalability. The model benefits from multi-behavior data, demonstrating the advantage of larger datasets and interactions for improved recommendation quality.
4. Evaluating HSTU on Ranking Tasks: For ranking tasks—where model success is measured using AUC and Logloss—the performance is contingent upon embedding sizes and architecture optimizations. It spotlights the importance of appropriately matching model complexity to dataset size to harness potential scaling benefits effectively.

Implications and Future Directions

The findings from this work illuminate several crucial insights into the development and application of large recommendation models:

• Implementation of Scaling Laws: The observed scaling laws in large recommendation models denote substantial possibilities for leveraging computational advancements to enhance recommendation performance, akin to trends seen in LLMs.
• Infrastructure and Resource Efficiency: Given the computational and storage demands of scaling, the paper suggests focusing on infrastructure optimizations and strategic data engineering to manage and alleviate environmental impacts.
• Continued Innovation in Model Architecture: The exploration of scalable, efficient model architectures remains a primary avenue for research, especially as future applications of recommendation models require increased complexity and nuance in data processing and feature integration.

In conclusion, this comprehensive paper provides a foundational understanding of the scaling potential within large recommendation models, opening pathways for subsequent research, particularly regarding efficiency optimization and domain-specific adaptations. Additionally, the insights draw attention to aligning data volume, model complexity, and architectural innovations to achieve optimal performance, paving the way for future exploration in complex, real-world data environments.