# RecCoT: Enhancing Recommendation via Chain-of-Thought

arXiv: https://arxiv.org/abs/2506.21032 · HTML: https://mlsanity.com/paper/2506.21032/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [SCoTER: Structured Chain-of-Thought Transfer for Enhanced Recommendation](https://mlsanity.com/paper/2511.19514/) (2025)
- [SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation](https://mlsanity.com/paper/2605.17648/) (2026)
- [Intuition-Guided Latent Reasoning for LLM-Based Recommendation](https://mlsanity.com/paper/2606.27684/) (2026)

## Abstract (arXiv)

In real-world applications, users always interact with items in multiple aspects, such as through implicit binary feedback (e.g., clicks, dislikes, long views) and explicit feedback (e.g., comments, reviews). Modern recommendation systems (RecSys) learn user-item collaborative signals from these implicit feedback signals as a large-scale binary data-streaming, subsequently recommending other highly similar items based on users' personalized historical interactions. However, from this collaborative-connection perspective, the RecSys does not focus on the actual content of the items themselves but instead prioritizes higher-probability signals of behavioral co-occurrence among items. Consequently, under this binary learning paradigm, the RecSys struggles to understand why a user likes or dislikes certain items. To alleviate it, some works attempt to utilize the content-based reviews to capture the semantic knowledge to enhance recommender models. However, most of these methods focus on predicting the ratings of reviews, but do not provide a human-understandable explanation.