# Leveraging LLM Reasoning Enhances Personalized Recommender Systems

arXiv: https://arxiv.org/abs/2408.00802 · HTML: https://mlsanity.com/paper/2408.00802/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Builds on (most cited first, 23 of 23)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies](https://mlsanity.com/paper/2101.02235/) (2021)
- [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://mlsanity.com/paper/1905.13319/) (2019)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://mlsanity.com/paper/2210.01240/) (2022)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning](https://mlsanity.com/paper/2212.07919/) (2022)
- [Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach](https://mlsanity.com/paper/2305.07001/) (2023)
- [M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems](https://mlsanity.com/paper/2205.08084/) (2022)
- [Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction](https://mlsanity.com/paper/2305.06474/) (2023)
- [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://mlsanity.com/paper/2206.09363/) (2022)
- [Iteratively Prompt Pre-trained Language Models for Chain of Thought](https://mlsanity.com/paper/2203.08383/) (2022)
- [Leveraging Large Language Models in Conversational Recommender Systems](https://mlsanity.com/paper/2305.07961/) (2023)
- [LLMRec: Benchmarking Large Language Models on Recommendation Task](https://mlsanity.com/paper/2308.12241/) (2023)

## Built on by (most cited first, 28 of 28)

- [OneRec-Think: In-Text Reasoning for Generative Recommendation](https://mlsanity.com/paper/2510.11639/) (2025)
- [Reinforced Latent Reasoning for LLM-based Recommendation](https://mlsanity.com/paper/2505.19092/) (2025)
- [Reason4Rec: Deliberative User Preference Alignment of Large Language Models for Recommendation](https://mlsanity.com/paper/2502.02061/) (2025)
- [R2ec: Towards Large Recommender Models with Reasoning](https://mlsanity.com/paper/2505.16994/) (2025)
- [Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem](https://mlsanity.com/paper/2409.07123/) (2024)
- [Memory Assisted LLM for Personalized Recommendation System](https://mlsanity.com/paper/2505.03824/) (2025)
- [PosterMate: Audience-driven Collaborative Persona Agents for Poster Design](https://mlsanity.com/paper/2507.18572/) (2025)
- [STAR: A Simple Training-free Approach for Recommendations using Large Language Models](https://mlsanity.com/paper/2410.16458/) (2024)
- [CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language](https://mlsanity.com/paper/2505.23837/) (2025)
- [Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation](https://mlsanity.com/paper/2504.02458/) (2025)
- [A Survey on Large Language Models in Multimodal Recommender Systems](https://mlsanity.com/paper/2505.09777/) (2025)
- [RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning](https://mlsanity.com/paper/2503.20430/) (2025)
- [Causal Reasoning in Pieces: Modular In-Context Learning for Causal Discovery](https://mlsanity.com/paper/2507.23488/) (2025)
- [ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning](https://mlsanity.com/paper/2604.07851/) (2026)
- [HADSF: Aspect Aware Semantic Control for Explainable Recommendation](https://mlsanity.com/paper/2510.26994/) (2025)
- [Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detection](https://mlsanity.com/paper/2511.21066/) (2025)
- [Verifiable Reasoning for LLM-based Generative Recommendation](https://mlsanity.com/paper/2603.07725/) (2026)
- [Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation](https://mlsanity.com/paper/2511.16478/) (2025)
- [Enriching Semantic Profiles into Knowledge Graph for Recommender Systems Using Large Language Models](https://mlsanity.com/paper/2601.08148/) (2026)
- [Don't Let Bandit Feedback Pull Continual LLM-Recommender Updates Off Target](https://mlsanity.com/paper/2605.18899/) (2026)
- [RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation](https://mlsanity.com/paper/2610.14474/) (2026)
- [From What to Why: Thought-Space Recommendation with Small Language Models](https://mlsanity.com/paper/2510.08626/) (2025)
- [On Reasoning Behind Next Occupation Recommendation](https://mlsanity.com/paper/2604.21204/) (2026)
- [RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation](https://mlsanity.com/paper/2605.07129/) (2026)
- [All Explanations are Wrong, But Many Are Useful: Exploring the Rashomon Explanation Set with Large Language Models](https://mlsanity.com/paper/2607.09502/) (2026)
- [Bridging the Information Gap: Semantic Densification and Hindsight Distillation for Cold-Start Prediction](https://mlsanity.com/paper/2607.17070/) (2026)
- [FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs](https://mlsanity.com/paper/2607.21624/) (2026)
- [Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders](https://mlsanity.com/paper/2608.20801/) (2026)

## Abstract (arXiv)

Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. While tasks like arithmetic reasoning involve clear, definitive answers and logical chains of thought, the application of LLM reasoning in recommendation systems (RecSys) presents a distinct challenge. RecSys tasks revolve around subjectivity and personalized preferences, an under-explored domain in utilizing LLMs' reasoning capabilities. Our study explores several aspects to better understand reasoning for RecSys and demonstrate how task quality improves by utilizing LLM reasoning in both zero-shot and finetuning settings. Additionally, we propose RecSAVER (Recommender Systems Automatic Verification and Evaluation of Reasoning) to automatically assess the quality of LLM reasoning responses without the requirement of curated gold references or human raters. We show that our framework aligns with real human judgment on the coherence and faithfulness of reasoning responses. Overall, our work shows that incorporating reasoning into RecSys can improve personalized tasks, paving the way for further advancements in recommender system methodologies.