# GOT4Rec: Graph of Thoughts for Sequential Recommendation

arXiv: https://arxiv.org/abs/2411.14922 · HTML: https://mlsanity.com/paper/2411.14922/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 22 Nov 2024.

## Placed in

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

## Builds on (most cited first, 3 of 3)

- [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024)
- [PALR: Personalization Aware LLMs for Recommendation](https://mlsanity.com/paper/2305.07622/) (2023)
- [E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation](https://mlsanity.com/paper/2312.02443/) (2023)

## Built on by (most cited first, 5 of 5)

- [Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2504.18383/) (2025)
- [Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications](https://mlsanity.com/paper/2510.21131/) (2025)
- [SCoTER: Structured Chain-of-Thought Transfer for Enhanced Recommendation](https://mlsanity.com/paper/2511.19514/) (2025)
- [Tree of Preferences for Diversified Recommendation](https://mlsanity.com/paper/2601.02386/) (2025)
- [WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models](https://mlsanity.com/paper/2607.26621/) (2026)

## Abstract (arXiv)

With their vast open-world knowledge and reasoning abilities, large language models (LLMs) have become a promising tool for sequential recommendation. Researchers have explored various methods to harness these capabilities, but most existing approaches rely on simple input-output prompting, failing to effectively bridge the gap between LLMs' general knowledge and the specific needs of recommendation tasks. While reasoning strategies like chain-of-thought (CoT) have been introduced to enhance performance, they often produce inaccurate recommendations due to underutilized user preference information and insufficient reasoning depth. To address these challenges, we propose GOT4Rec, a novel sequential recommendation method leveraging the graph of thoughts (GoT) reasoning strategy. Our method focuses on three key types of information in user histories: short-term interests, long-term interests and collaborative information from other users. It enables LLMs to reason independently and generate recommendations, subsequently aggregating results to derive final items. This method allows LLMs, with enhanced reasoning capabilities, to better utilize the user sequence information, producing more accurate recommendations and comprehensive explanations. Extensive experiments on real-world datasets demonstrate the effectiveness of GOT4Rec, outperforming existing state-of-the-art baselines with an average improvement of 37.11%. Our code is available at https://anonymous.4open.science/r/GOT4Rec.