Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenization (2412.13771v1)

Abstract: LLMs, endowed with exceptional reasoning capabilities, are adept at discerning profound user interests from historical behaviors, thereby presenting a promising avenue for the advancement of recommendation systems. However, a notable discrepancy persists between the sparse collaborative semantics typically found in recommendation systems and the dense token representations within LLMs. In our study, we propose a novel framework that harmoniously merges traditional recommendation models with the prowess of LLMs. We initiate this integration by transforming ItemIDs into sequences that align semantically with the LLMs space, through the proposed Alignment Tokenization module. Additionally, we design a series of specialized supervised learning tasks aimed at aligning collaborative signals with the subtleties of natural language semantics. To ensure practical applicability, we optimize online inference by pre-caching the top-K results for each user, reducing latency and improving effciency. Extensive experimental evidence indicates that our model markedly improves recall metrics and displays remarkable scalability of recommendation systems.

Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenization

This paper presents a sophisticated framework designed to enhance recommendation systems by effectively integrating them with LLMs. The researchers address the inherent challenge of aligning sparse collaborative semantics of traditional recommendation systems with the dense, token-based representation employed by LLMs. They propose a novel two-stage framework that bridges this semantic gap and optimizes the recommendation process.

Framework Overview

The proposed framework consists of two primary components: Alignment Tokenization and Alignment Task. These components work in tandem to align recommendation systems with LLMs, thereby achieving what the authors term "semantic convergence."

1. Alignment Tokenization: This component addresses the inefficiency and sparsity issues of training LLMs by transforming large-scale item spaces into smaller, manageable discrete token representations. Each item is represented by tokens derived from a CodeBooks structure, with layers hierarchically arranged to map items via residual approximations. Additionally, an LLM alignment loss is incorporated to ensure these token representations align with the LLM's semantic space, enhancing accuracy in semantic interpretation during inference and fine-tuning stages.
2. Alignment Task: This component enhances the LLM's ability to predict user interests by incorporating behavioral and semantic signals. It includes specialized supervised tasks such as sequential alignment and text alignment, tailored to reinforce semantic understanding in the recommendation context. The approach also integrates negative sampling strategies to combat sample selection bias and increase the model's robustness.

Empirical Evaluation

The authors demonstrate the efficacy of their approach through comprehensive experiments conducted on datasets like "Games", "Arts", and "Instruments" from the Amazon review database. The proposed method outperforms baseline models, including traditional recommendation systems and recent LLM-enhanced models. The improvement is particularly pronounced in scenarios with longer user interaction sequences, where the model can leverage the rich semantic context provided by LLMs.

The results are quantified via recall metrics such as Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). For example, the proposed model shows an HR@10 improvement of approximately 9.17% on the Games dataset compared to the best baseline, substantiating the enhanced recall capability of the integrated system.

Theoretical and Practical Implications

The integration of LLMs with recommendation systems promises to bring substantial advances in handling complex user interaction data by leveraging the semantic processing capabilities of LLMs. This framework provides a scalable solution that can be adapted to various recommendation tasks, offering a robust method for improved personalization and user satisfaction in digital platforms.

Future developments in AI could expand on this foundation, incorporating more diverse data modalities or further refining alignment techniques to reduce computational costs and improve real-time processing capabilities. The exploration of larger LLMs and refined pre-training techniques could unlock even greater potential in semantic understanding and user intent prediction in recommendation systems.

Concluding Remarks

This paper makes significant contributions to the domain of recommender systems, merging traditional collaborative filtering approaches with cutting-edge LLMing techniques. This integration not only enhances the precision and scalability of recommendations but also sets the stage for richer, more context-sensitive interaction models in the future of AI-driven personalization systems.