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Leveraging Large Language Models for Pre-trained Recommender Systems

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arxiv 2308.10837 v1 pith:DNCC5E4E submitted 2023-08-21 cs.IR

Zhixuan Chu , Hongyan Hao , Xin Ouyang , Simeng Wang , Yan Wang , Yue Shen , Jinjie Gu , Qing Cui
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Longfei Li Siqiao Xue James Y Zhang Sheng Li
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classification cs.IR
keywords recommendationrecsysllmsystemsknowledgelanguagemodelspre-trainedintegrating
verification ladder T0 review T1 audit T2 compute T3 formal

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Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectively integrating LLM's commonsense knowledge and reasoning abilities into recommendation systems remains a challenging problem. In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs. RecSysLLM retains LLM reasoning and knowledge while integrating recommendation domain knowledge through unique designs of data, training, and inference. This allows RecSysLLM to leverage LLMs' capabilities for recommendation tasks in an efficient, unified framework. We demonstrate the effectiveness of RecSysLLM on benchmarks and real-world scenarios. RecSysLLM provides a promising approach to developing unified recommendation systems by fully exploiting the power of pre-trained language models.

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Forward citations

Cited by 6 Pith papers

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

  1. Topology-Aware Tokenization for Generative Recommendation

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    TopoTok preserves item-neighbor structure in RQ-VAE tokenization via three granularity-matched distillation losses, improving generative-recommendation Recall@5 by up to 9.42% relative.

  2. Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation

    cs.IR 2025-06 conditional novelty 5.0 of 10

    A multi-task, multi-head item-to-item retrieval system that merges co-engagement candidates with semantically relevant candidates achieves both higher recall and higher semantic relevance than prior models.

  3. Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects

    cs.IR 2025-06 conditional novelty 4.0 of 10

    A survey classifying multi-objective recommendation research that uses generative AI into objective families, with metrics, datasets, and future directions.

  4. Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap

    cs.IR 2025-01 conditional novelty 4.0 of 10

    A comprehensive survey and roadmap that groups cold-start recommendation methods into four knowledge scopes and defines nine cold-start problem types.

  5. ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning

    cs.AI 2024-12 reject novelty 4.0 of 10

    A role-based multi-agent framework with a monitor that triggers re-planning is reported to outperform other LLM agent systems on two QA benchmarks, but no code, data, or error bars are provided.

  6. Improving the Performance of Sequential Recommendation Systems with an Extended Large Language Model

    cs.IR 2025-07 conditional novelty 2.0 of 10

    Replacing Llama2 with Llama3.1 in the LlamaRec framework improved recommendation metrics by 8 to 39 percent on three public datasets.

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