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Recommender Systems in the Era of Large Language Models (LLMs)
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With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an important component of our daily life, providing personalized suggestions that cater to user preferences. While Deep Neural Networks (DNNs) have made significant advancements in enhancing recommender systems by modeling user-item interactions and incorporating textual side information, DNN-based methods still face limitations, such as difficulties in understanding users' interests and capturing textual side information, inabilities in generalizing to various recommendation scenarios and reasoning on their predictions, etc. Meanwhile, the emergence of Large Language Models (LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI), due to their remarkable abilities in fundamental responsibilities of language understanding and generation, as well as impressive generalization and reasoning capabilities. As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems. Given the rapid evolution of this research direction in recommender systems, there is a pressing need for a systematic overview that summarizes existing LLM-empowered recommender systems, to provide researchers in relevant fields with an in-depth understanding. Therefore, in this paper, we conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting. More specifically, we first introduce representative methods to harness the power of LLMs (as a feature encoder) for learning representations of users and items. Then, we review recent techniques of LLMs for enhancing recommender systems from three paradigms, namely pre-training, fine-tuning, and prompting. Finally, we comprehensively discuss future directions in this emerging field.
Forward citations
Cited by 9 Pith papers
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Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations
LLMs exhibit mid-layer representation advantage for recommendations; MARC compresses representations modularly to reduce costs while improving performance, as shown in a large-scale online advertising deployment.
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Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
FreLLM4Rec shows that LLMs attenuate low-frequency collaborative components of item embeddings and introduces graph and temporal low-pass filters that preserve them, achieving up to 8% NDCG@10 gains.
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Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations
LAAC uses an LLM as a reference policy in an adversarial actor-critic setup, with regularization that keeps untested suggestions grounded, improving diversity, novelty, and accuracy on MovieLens-1M.
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RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
RankFlow deploys four LLM roles in sequence to rewrite queries, generate pseudo-answers, summarize passages, and rerank candidates, outperforming prior methods on TREC-DL, BEIR, and NovelEval.
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CFALR: Collaborative Filtering-Augmented Large Language Model for Personalized Fashion Outfit Recommendation
CFALR augments LLMs with collaborative filtering embeddings via trainable projection layers to outperform prior CF and LLM methods on Polyvore and IQON for personalized outfit tasks.
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Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking
Ocean4Rec uses offline LLM to create OCEAN profiles for items and time-decayed user profiles for request-time numeric reranking, improving NDCG@20 by 7.6% and 61.5% over base+recency in offline VOD evaluations.
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LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents
On CUAD legal contracts, a prompt-engineered QWEN-2 pipeline with chunking and two answer-selection heuristics reportedly outperforms the fine-tuned DeBERTa-large baseline by about 9%, reaching claimed state-of-the-ar...
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TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation
TrackRec trains a small LLM to generate user-preference summaries and a validator to score them, alternating the training so each improves the other, and reports gains on public and industrial recommendation benchmarks.
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R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
R4ec trains a small reflection model to critique and refine LLM-generated user and item knowledge, which then improves downstream recommendation accuracy.
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