Adaptive In-Context Learning with Large Language Models for Bundle Generation
Authors: 
Zhu Sun, Kaidong Feng, Jie Yang, Xinghua Qu, Hui Fang, Yew-Soon Ong, Wenyuan LiuAuthors Info & Claims
Zhu Sun, Kaidong Feng, Jie Yang, Xinghua Qu, Hui Fang, Yew-Soon Ong, Wenyuan LiuAuthors Info & ClaimsPages 966 - 976
Abstract
Most existing bundle generation approaches fall short in generating fixed-size bundles. Furthermore, they often neglect the underlying user intents reflected by the bundles in the generation process, resulting in less intelligible bundles. This paper addresses these limitations through the exploration of two interrelated tasks, i.e., personalized bundle generation and the underlying intent inference, based on different user sessions. Inspired by the reasoning capabilities of large language models (LLMs), we propose an adaptive in-context learning paradigm, which allows LLMs to draw tailored lessons from related sessions as demonstrations, enhancing the performance on target sessions. Specifically, we first employ retrieval augmented generation to identify nearest neighbor sessions, and then carefully design prompts to guide LLMs in executing both tasks on these neighbor sessions. To tackle reliability and hallucination challenges, we further introduce (1) a self-correction strategy promoting mutual improvements of the two tasks without supervision signals and (2) an auto-feedback mechanism for adaptive supervision based on the distinct mistakes made by LLMs on different neighbor sessions. Thereby, the target session can gain customized lessons for improved performance by observing the demonstrations of its neighbor sessions. Experiments on three real-world datasets demonstrate the effectiveness of our proposed method.
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Index Terms
- Adaptive In-Context Learning with Large Language Models for Bundle Generation
Recommendations
Large Language Models for Intent-Driven Session Recommendations
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalThe goal of intent-aware session recommendation (ISR) approaches is to capture user intents within a session for accurate next-item prediction. However, the capability of these approaches is limited by assuming all sessions have a uniform and fixed ...
Personalized Bundle List Recommendation
WWW '19: The World Wide Web ConferenceProduct bundling, offering a combination of items to customers, is one of the marketing strategies commonly used in online e-commerce and offline retailers. A high-quality bundle generalizes frequent items of interest, and diversity across bundles ...
Tutorial on Large Language Models for Recommendation
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsFoundation Models such as Large Language Models (LLMs) have significantly advanced many research areas. In particular, LLMs offer significant advantages for recommender systems, making them valuable tools for personalized recommendations. For example, ...
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July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
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- Guido Zuccon,
- Yi Zhang
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Published: 11 July 2024
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- Shanghai Rising-Star Program
- Data Science and Artificial Intelligence Research Centre, School of Computer Science and Engineering at the Nanyang Technological University (NTU), Singapore
- National Natural Science Foundation of China
- A*Star Center for Frontier Artificial Intelligence Research
- Natural Science Foundation of Shanghai
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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 14 - 18, 2024
Washington DC, USA
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