Controllable Multi-Behavior Recommendation for In-Game Skins with Large Sequential Model-Reference-Cited by-同舟云学术

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Controllable Multi-Behavior Recommendation for In-Game Skins with Large Sequential Model

Published:2024-08-24 Issue: Volume:9 Page:4986-4996
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Gou Yanjie¹^ORCID,Yao Yuanzhou²^ORCID,Zhang Zhao³^ORCID,Wu Yiqing³^ORCID,Hu Yi¹^ORCID,Zhuang Fuzhen⁴^ORCID,Liu Jiangming⁵^ORCID,Xu Yongjun³^ORCID

Affiliation:

1. Common Data Platform, Tencent, Shenzhen, China

2. Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China

3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

4. Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China

5. School of Information Science and Engineering, Yunnan University, Kunming, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671572

Reference37 articles.

1. Graph Heterogeneous Multi-Relational Recommendation

2. Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

3. Multi-Behavior Recommendation with Cascading Graph Convolution Networks

4. George H Dunteman. 1989. Principal components analysis. Vol. 69. Sage.

5. Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023. Recommender Systems in the Era of Large Language Models (LLMs). (Jul 2023).

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