mlsanityData to 14 Oct 2026

M-LLM3REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs

arXiv 2508.15262 ↗First posted 21 Aug 2025cs.IR cs.AIcited by 1

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Abstract (arXiv)

Recommendation systems have been essential for both user experience and platform efficiency by alleviating information overload and supporting decision-making. Traditional methods, i.e., content-based filtering, collaborative filtering, and deep learning, have achieved impressive results in recommendation systems. However, the cold-start and sparse-data scenarios are still challenging to deal with. Existing solutions either generate pseudo-interaction sequence, which often introduces redundant or noisy signals, or rely heavily on semantic similarity, overlooking dynamic shifts in user motivation. To address these limitations, this paper proposes a novel recommendation framework, termed M-LLM3REC, which leverages large language models for deep motivational signal extraction from limited user interactions. M-LLM3REC comprises three integrated modules: the Motivation-Oriented Profile Extractor (MOPE), Motivation-Oriented Trait Encoder (MOTE), and Motivational Alignment Recommender (MAR). By emphasizing motivation-driven semantic modeling, M-LLM3REC demonstrates robust, personalized, and generalizable recommendations, particularly boosting performance in cold-start situations in comparison with the state-of-the-art frameworks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 40 references that are in the map. First posted on arXiv 21 Aug 2025.

Builds on

23 of 40 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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11 more
  1. 2022M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systemscited by 152
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  5. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  6. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
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  11. 2025Structured Prompting and Feedback-Guided Reasoning with LLMs for Data Interpretationcited by 5

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Citing papers per year: 2026: 1.

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  1. 2026Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenderscited by 0