Sequential Recommendation with Latent Relations based on Large Language Model-Reference-Cited by-同舟云学术

Sequential Recommendation with Latent Relations based on Large Language Model

Published:2024-07-10 Issue: Volume:35 Page:335-344
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Yang Shenghao¹^ORCID,Ma Weizhi²^ORCID,Sun Peijie³^ORCID,Ai Qingyao³^ORCID,Liu Yiqun³^ORCID,Cai Mingchen⁴^ORCID,Zhang Min¹^ORCID

Affiliation:

1. DCST, Tsinghua University, Quan Cheng Laboratory, Beijing, China

2. AIR, Tsinghua University, Beijing, China

3. DCST, Tsinghua University, Beijing, China

4. Meituan, Beijing, China

Funder

Quan Cheng Laboratory

Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657762

Reference47 articles.

1. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

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

3. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901.

4. Yixin Cao Xiang Wang Xiangnan He Zikun Hu and Tat-Seng Chua. 2019. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In The world wide web conference. 151--161.

5. Sequential Recommendation with Graph Neural Networks

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