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

TL;DR

LLM-KT is a flexible, model-agnostic framework that enhances collaborative filtering models by integrating LLM-generated features into intermediate layers, improving recommendation performance across various datasets.

Contribution

Introduces a novel, adaptable framework for knowledge transfer from LLMs to CF models without architectural modifications, broadening application scope.

Findings

Consistently improves baseline CF models on MovieLens and Amazon datasets.

Competitive with state-of-the-art in context-aware recommendation settings.

Applicable to a wide range of CF models without requiring structural changes.

Abstract

We present LLM-KT, a flexible framework designed to enhance collaborative filtering (CF) models by seamlessly integrating LLM (Large Language Model)-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.