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Recommender Systems in the Era of Large Language Models (LLMs)

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arxiv 2307.02046 v6 pith:L3JCSEXD submitted 2023-07-05 cs.IR cs.AIcs.CL

Zihuai Zhao , Wenqi Fan , Jiatong Li , Yunqing Liu , Xiaowei Mei , Yiqi Wang , Zhen Wen , Fei Wang
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Xiangyu Zhao Jiliang Tang Qing Li
This is my paper
classification cs.IRcs.AIcs.CL
keywords recommendersystemsllmslanguageunderstandingenhancingfieldsfine-tuning
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation

    cs.CL 2025-08 conditional novelty 6.0 of 10

    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.

  2. Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations

    cs.LG 2025-07 conditional novelty 6.0 of 10

    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.

  3. Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning

    cs.CL 2025-07 conditional novelty 6.0 of 10

    CETRec improves LLM-based sequential recommendation by adding item-level temporal embeddings and a counterfactual tuning loss that rewards different predictions when temporal order is erased.

  4. LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents

    cs.AI 2025-09 reject novelty 5.0 of 10

    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...

  5. TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation

    cs.IR 2025-08 conditional novelty 5.0 of 10

    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.

  6. R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems

    cs.IR 2025-07 conditional novelty 5.0 of 10

    R4ec trains a small reflection model to critique and refine LLM-generated user and item knowledge, which then improves downstream recommendation accuracy.

  7. CoDec: Prefix-Shared Decoding Kernel for LLMs

    cs.LG 2025-05 conditional novelty 5.0 of 10

    CoDec combines KV-cache reads across requests that share a prefix, yielding average 1.9x decode-attention speedup and 120.9x less global memory traffic versus FlashDecoding.

  8. LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

    cs.IR 2025-06 conditional novelty 4.0 of 10

    A KG-enhanced LlamaRec that feeds user-specific relation paths into a Llama-2 ranker reports modest MRR, NDCG, and Recall gains on two benchmarks.

  9. Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion

    cs.IR 2025-06 conditional novelty 4.0 of 10

    Exp4Fuse improves sparse retrieval by fusing the ranked lists from the original query and an LLM-expanded query using a modified reciprocal rank fusion.

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