LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering (2411.00556v1)

Abstract: We present LLM-KT, a flexible framework designed to enhance collaborative filtering (CF) models by seamlessly integrating LLM-generated features. Unlike existing methods that rely on passing LLM-generated features as direct inputs, our framework injects these features into an intermediate layer of any CF model, allowing the model to reconstruct and leverage the embeddings internally. This model-agnostic approach works with a wide range of CF models without requiring architectural changes, making it adaptable to various recommendation scenarios. Our framework is built for easy integration and modification, providing researchers and developers with a powerful tool for extending CF model capabilities through efficient knowledge transfer. We demonstrate its effectiveness through experiments on the MovieLens and Amazon datasets, where it consistently improves baseline CF models. Experimental studies showed that LLM-KT is competitive with the state-of-the-art methods in context-aware settings but can be applied to a broader range of CF models than current approaches.

• The paper introduces LLM-KT, which integrates LLM-generated features into intermediate layers of CF models to achieve up to 21% improvements in metrics like NDCG@10 and Recall@10.
• The methodology employs a two-phase training process with an auxiliary pretext task that reconstructs LLM-generated profiles without altering existing model architectures.
• Experimental validation on datasets like MovieLens and Amazon demonstrates LLM-KT’s competitive performance and scalability in both traditional and context-aware recommendation settings.

An Analytical Overview of LLM-KT: A Framework for Knowledge Transfer in Collaborative Filtering

The paper "LLM-KT: A Versatile Framework for Knowledge Transfer from LLMs to Collaborative Filtering" introduces a new methodology for enhancing Collaborative Filtering (CF) models by incorporating features generated by LLMs. The motivation behind this research arises from the limitations of conventional CF models, which often struggle to capture complex user-item interactions. By leveraging LLMs, the proposed framework aims to provide a richer representation of knowledge, enhancing the adaptability and accuracy of recommendations.

Methodology Overview

The LLM-KT framework is characterized by its versatility and model-agnostic approach. Unlike existing methods, which integrate LLM-generated features as direct inputs, LLM-KT embeds these features into intermediate layers of CF models. This technique does not mandate modifications to the underlying architecture, thereby broadening its applicability across various CF models. By embedding features internally, the framework facilitates a more nuanced understanding of user preferences through a two-phase training process—initial knowledge transfer followed by fine-tuning.

Key components of this approach include:

• Profile Generation: LLMs generate concise preference descriptions—referred to as "profiles"—based on user interaction data. These profiles are subsequently transformed into dense embeddings, employing models like "text-embedding-ada-002" in the experiments.
• Pretext Task Training: The framework incorporates an auxiliary pretext task where the CF model is trained to reconstruct these LLM-generated profiles within its intermediate layers. This task is governed by a combined loss function, promoting effective knowledge transfer while preserving the model's original objectives.
• Seamless Integration: Built atop RecBole, the framework supports comprehensive experimentation, allowing researchers to define complex configurations and tailor the pipeline to specific scenarios.

Experimental Validation

The framework's effectiveness was validated on standard datasets such as MovieLens and Amazon's "CD and Vinyl" category. In traditional CF settings, significant improvements were noted across metrics like NDCG@10 and Recall@10, with up to 21% enhancement, particularly in the context of models like NeuMF and SimpleX. In context-aware settings, LLM-KT exhibited performance competitive with state-of-the-art methods such as KAR, as measured by AUC-ROC metrics in CTR prediction tasks. These results underscore the framework's capability to offer a versatile solution for enhancing CF models without architectural dependencies.

Implications and Future Directions

LLM-KT represents a promising direction in the integration of LLMs with recommendation systems, emphasizing the importance of internal feature reconstruction over direct input transformation. Practically, this framework can be instrumental for practitioners seeking scalable solutions that augment existing CF models with minimal intervention. Theoretically, it opens avenues for exploring novel forms of knowledge representation and transfer in machine learning models.

Future research may explore its application in sequential recommendation tasks and other domains, further refining the framework's applicability. Additionally, exploring alternate architectures for profile generation and incorporation could yield insights into optimizing knowledge transfer processes. As the landscape of AI continues to evolve, frameworks like LLM-KT will likely play a pivotal role in bridging the gap between language processing and personalized recommendation systems.