Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System

By Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, Jiawei Zhang. arXiv.org, 2023

502 citations recorded by the source corpus.

Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks. However, traditional recommender systems continue to face great challenges such as poor interactivity and explainability, which actually also hinder their broad deployment in real-world systems. To address these limitations, this paper proposes a novel paradigm called Chat-Rec (ChatGPT Augmented Recommender System) that innovatively augments LLMs for building conversational recommender systems by converting user profiles and historical interactions into prompts. Chat-Rec is demonstrated to be effective in learning user preferences and establishing connections between users and products through in-context learning, which also makes the recommendation process more interactive and explainable. What's more, within the Chat-Rec framework, user's preferences can transfer to different products for cross-domain recommendations, and prompt-based injection of information into LLMs can also handle the cold-start scenarios with new items. In our experiments, Chat-Rec effectively improve the results of top-k recommendations and performs better in zero-shot rating prediction task. Chat-Rec offers a novel approach to improving recommender systems and presents new practical scenarios for the implementation of AIGC (AI generated content) in recommender system studies.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/0cfdd655100055f234fd23ebecd915504b8e00e3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Wayne Xin Zhao
3. Yupeng Hou
4. Fuli Feng
5. Chao Huang
6. Lianghao Xia
7. Dawei Yin
8. Jiliang Tang

In Information Systems

1. Xiangnan He
2. Wayne Xin Zhao
3. Yupeng Hou
4. Fuli Feng
5. Chao Huang
6. Lianghao Xia
7. Hongyu Lu
8. Dawei Yin

Citations in the corpus

• Is ChatGPT a Good Recommender? A Preliminary Study (2023, background): “Chat-REC [17], a concurrent work closely related to our study, leverages ChatGPT as an interface for conversational recommendations, thereby augmenting the performance of existing recommender models and rendering the recommendation process more interactive and explainable.”
• Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs (2023, methodology): “In addition to the knowledge learned via pre-training, recent studies suggest that LLMs present pre-liminary success on tasks with implicit graph structures such as recommendation [35; 14], ranking [26], and multi-hop reasoning [7], in which LLMs are adopted to make the final predictions.”
• TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation (2023, methodology): “In recent initial attempts [11, 46], achieving the target relies on In-context Learning [2], which is typically implemented through the official OpenAI API [1].”
• Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023, unknown): “• Vague intention ( 𝐼 1 ) .”
• Retrieval-Augmented Generation for Large Language Models: A Survey (2023, methodology): “Consequently, the model’s own outputs are utilized instead of the training data [ Wang et al. , 2022a ] .”
• Recommender Systems in the Era of Large Language Models (LLMs) (2023, unknown): “One such example is Chat-Rec [3], which leverages ChatGPT to provide clear and comprehensible reasoning behind its suggestions, thereby fostering trust and user engagement.”
• Large Language Models are Zero-Shot Rankers for Recommender Systems (2023, background): “Indeed, there are some preliminary attempts that employ LLMs for solving recommendation tasks [9, 29, 30, 5, 21, 34].”
• Representation Learning with Large Language Models for Recommendation (2023, methodology): “This indicates that there are limitations when blindly using LLMs to improve the re-ranking process in recommendation systems that heavily rely on textual information.”
• When large language models meet personalization: perspectives of challenges and opportunities (2023, unknown): “To address this challenge, there are currently two approaches being explored: fine-tuning [215] and tool learning [225, 226].”
• Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation (2023, unknown): “A common approach is to represent user behaviors as text sequences ( e.g., by concatenating the titles of historical items), and then design prompts to guide LLMs to perform the recommendation task [31], [32].”