# ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning

arXiv: https://arxiv.org/abs/2410.23180 · HTML: https://mlsanity.com/paper/2410.23180/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Placed in

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

## Builds on (most cited first, 6 of 6)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction](https://mlsanity.com/paper/2305.06474/) (2023)
- [PALR: Personalization Aware LLMs for Recommendation](https://mlsanity.com/paper/2305.07622/) (2023)
- [Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models](https://mlsanity.com/paper/2306.10933/) (2023)

## Built on by (most cited first, 14 of 14)

- [Reinforced Latent Reasoning for LLM-based Recommendation](https://mlsanity.com/paper/2505.19092/) (2025)
- [A Survey on LLM Inference-Time Self-Improvement](https://mlsanity.com/paper/2412.14352/) (2024)
- [LLMs for Customized Marketing Content Generation and Evaluation at Scale](https://mlsanity.com/paper/2506.17863/) (2025)
- [MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMs](https://mlsanity.com/paper/2510.14629/) (2025)
- [RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning](https://mlsanity.com/paper/2503.20430/) (2025)
- [GRAVITY: A Framework for Personalized Text Generation via Profile-Grounded Synthetic Preferences](https://mlsanity.com/paper/2510.11952/) (2025)
- [Language Models as Semantic Augmenters for Sequential Recommenders](https://mlsanity.com/paper/2510.18046/) (2025)
- [Is Active Persona Inference Necessary for Aligning Small Models to Personal Preferences?](https://mlsanity.com/paper/2505.13257/) (2025)
- [Reasoning-Based Personalized Generation for Users with Sparse Data](https://mlsanity.com/paper/2602.21219/) (2026)
- [TRACE: Tourism Recommendation with Accountable Citation Evidence](https://mlsanity.com/paper/2605.07677/) (2026)
- [Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation](https://mlsanity.com/paper/2504.11658/) (2025)
- [From latent factors to language: a user study on LLM-generated explanations for an inherently interpretable matrix-based recommender system](https://mlsanity.com/paper/2509.18980/) (2025)
- [From What to Why: Thought-Space Recommendation with Small Language Models](https://mlsanity.com/paper/2510.08626/) (2025)
- [Unifying Inductive, Cross-Domain, and Multimodal Learning for Robust and Generalizable Recommendation](https://mlsanity.com/paper/2510.21812/) (2025)

## Abstract (arXiv)

This paper presents ReasoningRec, a reasoning-based recommendation framework that leverages Large Language Models (LLMs) to bridge the gap between recommendations and human-interpretable explanations. In contrast to conventional recommendation systems that rely on implicit user-item interactions, ReasoningRec employs LLMs to model users and items, focusing on preferences, aversions, and explanatory reasoning. The framework utilizes a larger LLM to generate synthetic explanations for user preferences, subsequently used to fine-tune a smaller LLM for enhanced recommendation accuracy and human-interpretable explanation. Our experimental study investigates the impact of reasoning and contextual information on personalized recommendations, revealing that the quality of contextual and personalized data significantly influences the LLM's capacity to generate plausible explanations. Empirical evaluations demonstrate that ReasoningRec surpasses state-of-the-art methods by up to 12.5% in recommendation prediction while concurrently providing human-intelligible explanations. The code is available here: https://github.com/millenniumbismay/reasoningrec.