Item-Language Model for Conversational Recommendation

TL;DR

This paper introduces the Item-Language Model (ILM), combining an item encoder with a frozen LLM to improve conversational recommendation by effectively integrating user interaction signals with language understanding.

Contribution

The paper proposes a novel ILM architecture that encodes user interactions into text-aligned representations and leverages a frozen pretrained LLM, addressing data availability and knowledge retention issues.

Findings

Language-aligned item representations improve recommendation quality.

User interaction signals encoded effectively enhance model performance.

ILM preserves pretrained language and reasoning abilities while incorporating interaction data.

Abstract

Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abilities have been extended with multi-modality to include image, audio, and video capabilities. Recommender systems, on the other hand, have been critical for information seeking and item discovery needs. Recently, there have been attempts to apply LLMs for recommendations. One difficulty of current attempts is that the underlying LLM is usually not trained on the recommender system data, which largely contains user interaction signals and is often not publicly available. Another difficulty is user interaction signals often have a different pattern from natural language text, and it is currently unclear if the LLM training setup can learn more non-trivial knowledge from interaction signals compared with traditional recommender system methods. Finally, it is difficult to train multiple LLMs for different use-cases, and to retain the original language and reasoning abilities when learning from recommender system data. To address these three limitations, we propose an Item-Language Model (ILM), which is composed of an item encoder to produce text-aligned item representations that encode user interaction signals, and a frozen LLM that can understand those item representations with preserved pretrained knowledge. We conduct extensive experiments which demonstrate both the importance of the language-alignment and of user interaction knowledge in the item encoder.