Adaptive In-Context Learning with Large Language Models for Bundle Generation-Reference-Cited by-同舟云学术

Adaptive In-Context Learning with Large Language Models for Bundle Generation

Published:2024-07-10 Issue: Volume:1215 Page:966-976
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Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Sun Zhu¹^ORCID,Feng Kaidong²^ORCID,Yang Jie³^ORCID,Qu Xinghua⁴^ORCID,Fang Hui⁵^ORCID,Ong Yew-Soon⁶^ORCID,Liu Wenyuan²^ORCID

Affiliation:

1. A*STAR Centre for Frontier AI Research & Singapore University of Technology and Design, Singapore, Singapore

2. Yanshan University, Qinhuangdao, China

3. Delft University of Technology, Delft, Netherlands

4. Shanda AI-Lab & Tianqiao and Chrissy Chen Institute, Singapore, Singapore

5. Shanghai University of Finance and Economics, Shanghai, China

6. A*STAR Centre for Frontier AI Research & Nanyang Technological University, Singapore, Singapore

Funder

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

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657808

Reference74 articles.

1. Rakesh Agrawal, Ramakrishnan Srikant, et al. 1994. Fast algorithms for mining association rules. In Proceedings of 20th International Conference on Very Large Scale Data Bases (VLDB), Vol. 1215. 487--499.

2. BRUCE: Bundle Recommendation Using Contextualized item Embeddings

3. Personalized Bundle List Recommendation

4. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

5. Recommender systems for product bundling