MARM: Unlocking the Future of Recommendation Systems through Memory Augmentation and Scalable Complexity (2411.09425v2)

Abstract: Scaling-law has guided the LLM designing for past years, however, it is worth noting that the scaling laws of NLP cannot be directly applied to RecSys due to the following reasons: (1) The amount of training samples and model parameters is typically not the bottleneck for the model. Our recommendation system can generate over 50 billion user samples daily, and such a massive amount of training data can easily allow our model parameters to exceed 200 billion, surpassing many LLMs (about 100B). (2) To ensure the stability and robustness of the recommendation system, it is essential to control computational complexity FLOPs carefully. Considering the above differences with LLM, we can draw a conclusion that: for a RecSys model, compared to model parameters, the computational complexity FLOPs is a more expensive factor that requires careful control. In this paper, we propose our milestone work, MARM (Memory Augmented Recommendation Model), which explores a new cache scaling-laws successfully.

• The paper introduces MARM, a novel memory-augmented recommendation model that employs caching strategies to reduce computational complexity from O(n²*d) to O(n*d).
• The paper demonstrates a GAUC improvement of 0.43% and a 2.079% increase in play time, underscoring enhanced user engagement.
• The paper outlines a multi-layer target-attention module and sequence generator that enable efficient real-time recommendations using extensive user data.

Exploring MARM: Enhancing Recommendation Systems with Memory Augmentation and Scalable Complexity

This paper presents the MARM (Memory Augmented Recommendation Model), a novel approach in the field of recommendation systems aimed at overcoming computational bottlenecks through innovative caching strategies. Recognizing the fundamental differences between NLP models and recommendation systems, the authors propose an adaptation of scaling laws specifically tailored for recommendation systems.

Key Innovations

The authors identify a critical distinction: whereas scaling laws like those guiding the development of LLMs such as GPTs focus on optimizing parameter and data scales, these approaches cannot be directly applied to recommendation systems. This incongruence arises from different operational constraints and system requirements:

1. Data Abundance: Recommendation systems, as noted, can generate over 50 billion user samples daily, ensuring that data availability is not a bottleneck. In contrast, NLP models often grapple with finite data corpora that need iterative fine-tuning.
2. Computation versus Parameters: While the volume of learnable parameters may not pose a challenge in recommendation systems (with potential to surpass 200 billion), the computational complexity, particularly FLOPs, imposes a significant cost. These systems demand high efficiency for both training and swift online inferencing.

The proposed MARM model innovatively utilizes memory augmentation to manage these computational constraints. By caching components of complex module calculations, the model transitions from single-layer sequence interest modeling to a multi-layer approach with minimal increased computational burden, reducing time complexity from $\mathcal{O}(n^2*d)$ to $\mathcal{O}(n*d)$.

Methodological Implementation

MARM's implementation leverages a cache that facilitates memory augmentation throughout the recommendation process. The model extends current strategies by enabling rapid evolution and inference across complex datasets without sacrificing efficiency. Notably, MARM incorporates:

• Sequence Generator: Produces ordered sequences of user-interacted items, establishing a foundation for subsequent inference steps.
• Cache Memory Storage: An essential component that stores partial computation outcomes, using sophisticated hash strategies to access unique cache results effectively.
• Multi-layer Target-Attention Module: Facilitates reduced computational load through the layered processing of cached data relative to current user interactions, thus enhancing recommendation fidelity.

Numerical Evaluation and Outcomes

The empirical results demonstrate MARM's potential through meaningful metrics such as GAUC and play-time increases. Offline tests reveal 0.43% GAUC improvement, while field deployments reflect a notable 2.079% gain in user play time. These performance enhancements underscore the model's efficacy in leveraging stored computation to augment real-time user engagement.

Implications and Future Directions

The implications of MARM are manifold, suggesting a pivotal shift in how recommendation models can harness extensive user data while managing the computational expense of processing it. This paper plants the seed for exploring memory-centric methodologies, potentially serving as a bridge toward more comprehensive, data-enriched recommendation models suited for an array of digital environments.

For future endeavors, the exploration of broader applications across different recommendation platforms could be fruitful. Additionally, further refinement of the caching strategies, particularly in diverse use-case scenarios, may reveal even more profound insights into the scalability of memory augmentation technologies.

In conclusion, this work contributes significantly to the field by merging memory augmentation with scalable computational frameworks, presenting a sophisticated yet efficient avenue for evolving recommendation systems. Future research may well build upon these findings, driving continued innovation in the way user information is processed and recommendations are personalized.