Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator (2409.09253v1)

Abstract: Owing to the unprecedented capability in semantic understanding and logical reasoning, the pre-trained LLMs have shown fantastic potential in developing the next-generation recommender systems (RSs). However, the static index paradigm adopted by current methods greatly restricts the utilization of LLMs capacity for recommendation, leading to not only the insufficient alignment between semantic and collaborative knowledge, but also the neglect of high-order user-item interaction patterns. In this paper, we propose Twin-Tower Dynamic Semantic Recommender (TTDS), the first generative RS which adopts dynamic semantic index paradigm, targeting at resolving the above problems simultaneously. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender, hierarchically allocating meaningful semantic index for items and users, and accordingly predicting the semantic index of target item. Furthermore, a dual-modality variational auto-encoder is proposed to facilitate multi-grained alignment between semantic and collaborative knowledge. Eventually, a series of novel tuning tasks specially customized for capturing high-order user-item interaction patterns are proposed to take advantages of user historical behavior. Extensive experiments across three public datasets demonstrate the superiority of the proposed methodology in developing LLM-based generative RSs. The proposed TTDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.

• The paper presents TTDS, a recommender system that integrates semantic and collaborative knowledge via a twin-tower dynamic semantic token generator to enhance recommendation accuracy.
• It employs a dual-modality variational auto-encoder (DM-VAE) to align under-trained semantic tokens with pre-trained natural language tokens, improving model understanding and generation.
• The study introduces novel fine-tuning tasks to capture high-order user-item interactions, achieving average improvements of 19.41% in Hit-Rate and 20.84% in NDCG across multiple datasets.

Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator

In "Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator," the authors propose Twin-Tower Dynamic Semantic Recommender (TTDS), a novel generative recommender system (RS) designed to leverage the semantic understanding and logical reasoning capabilities of LLMs for enhanced recommendation accuracy.

Key Contributions

1. Dynamic Knowledge Fusion Framework: The TTDS recommender system integrates a dynamic semantic index mechanism, employing a twin-tower semantic token generator. This design hierarchically allocates meaningful semantic indices to items and users, thus enhancing the prediction of the target item's semantic index. The twin-tower architecture allows for a more robust fusion of semantic and collaborative knowledge, overcoming limitations of static index paradigms.
2. Dual-Modality Variational Auto-Encoder (DM-VAE): To mitigate discrepancies between under-trained semantic tokens and well pre-trained natural language (NL) tokens, a DM-VAE is introduced. This component facilitates a multi-grained alignment of semantic and collaborative knowledge, improving the model’s understanding and generation capabilities.
3. High-Order User-Item Interaction Patterns: The authors also design a series of novel fine-tuning tasks aimed at capturing complex and high-order user-item interaction patterns. These tasks leverage historical user behavior to provide richer and more accurate recommendation contexts.

Performance and Evaluation

The paper presents comprehensive empirical evidence demonstrating the superior performance of the proposed TTDS model across three public datasets: Instruments, Games, and Arts. The results indicate average improvements of 19.41% in Hit-Rate and 20.84% in NDCG compared to state-of-the-art baseline methods, showcasing the practical potential of TTDS in real-world applications.

Theoretical and Practical Implications

The TTDS recommender system represents a paradigm shift in applying LLMs to RSs. The dynamic knowledge fusion framework addresses several critical challenges, such as the alignment between semantic and collaborative information and the exploitation of high-order interaction patterns. This sets a new standard for integrating LLM capabilities into RSs, promising enhanced personalization and user satisfaction.

Speculative Future Developments

Looking forward, the dynamic semantic index approach in TTDS could inspire further research focused on:

• Extending the twin-tower architecture to incorporate more diverse data modalities, such as multimedia content, thereby broadening the applicability of generative RSs.
• Exploring more sophisticated vector quantization techniques and dynamic indexing mechanisms to further refine the balance between computational efficiency and expressiveness.
• Investigating real-time adaptation mechanisms that would allow RSs to dynamically update semantic indices based on user interactions in near real-time.

Conclusion

The TTDS recommender system represents a significant advancement in the field of generative RSs, leveraging LLMs' unparalleled semantic understanding and logical reasoning capabilities. Through a dynamic knowledge fusion framework, DM-VAE, and novel high-order interaction pattern tasks, the TTDS model achieves marked improvements over existing methods. These innovations not only enhance current RS capabilities but also open new avenues for future research and development in AI-driven recommendation systems.