GOT4Rec: Graph of Thoughts for Sequential Recommendation

TL;DR

GOT4Rec introduces a graph of thoughts reasoning strategy for sequential recommendation, enabling large language models to better utilize user history and improve recommendation accuracy through enhanced reasoning and aggregation.

Contribution

The paper presents GOT4Rec, a novel method that leverages graph of thoughts reasoning to improve LLM-based sequential recommendation by effectively integrating user interests and collaborative data.

Findings

Outperforms state-of-the-art baselines by 37.11% on real-world datasets.

Effectively utilizes user short-term, long-term, and collaborative interests.

Enhances recommendation accuracy and explanation quality.

Abstract

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.