One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems

TL;DR

This paper presents LLM-Rec, a domain-agnostic recommendation framework using large language models to generate rich, semantic user and item representations across multiple domains, addressing data sparsity and cold-start issues.

Contribution

It introduces a novel multi-domain recommendation approach leveraging pre-trained LLMs with behavior mixing and item title concatenation, enabling semantic knowledge transfer across domains.

Findings

LLM-Rec improves recommendation performance across multiple domains.

Semantic representations from LLMs enhance knowledge transfer.

Scaling laws from NLP also benefit recommendation tasks.

Abstract

Sequential recommendation systems aim to predict users' next likely interaction based on their history. However, these systems face data sparsity and cold-start problems. Utilizing data from other domains, known as multi-domain methods, is useful for alleviating these problems. However, traditional multi-domain methods rely on meaningless ID-based item representation, which makes it difficult to align items with similar meanings from different domains, yielding sup-optimal knowledge transfer. This paper introduces LLM-Rec, a framework that utilizes pre-trained large language models (LLMs) for domain-agnostic recommendation. Specifically, we mix user's behaviors from multiple domains and concatenate item titles into a sentence, then use LLMs for generating user and item representations. By mixing behaviors across different domains, we can exploit the knowledge encoded in LLMs to bridge the semantic across over multi-domain behaviors, thus obtaining semantically rich representations and improving performance in all domains. Furthermore, we explore the underlying reasons why LLMs are effective and investigate whether LLMs can understand the semantic correlations as the recommendation model, and if advanced techniques like scaling laws in NLP also work in recommendations. We conduct extensive experiments with LLMs ranging from 40M to 6.7B to answer the above questions and to verify the effectiveness of LLM-Rec in multi-domain recommendation.