Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Chen, Linxun"'
Autor:
Ning, Xuying, Xu, Wujiang, Liu, Xiaolei, Ha, Mingming, Ma, Qiongxu, Li, Youru, Chen, Linxun, Zhang, Yongfeng
Cross-Domain Sequential Recommendation (CDSR) methods aim to address the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CDSR methods typically rely on overlapping users, designing complex cro
Externí odkaz:
http://arxiv.org/abs/2405.20710
In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values and those missing values are generally missing-not-at-random, which deteriorates the prediction perform
Externí odkaz:
http://arxiv.org/abs/2405.15403
With the rapid development of Large Language Models (LLMs), various explorations have arisen to utilize LLMs capability of context understanding on recommender systems. While pioneering strategies have primarily transformed traditional recommendation
Externí odkaz:
http://arxiv.org/abs/2402.16539
Autor:
Xu, Wujiang, Wu, Qitian, Wang, Runzhong, Ha, Mingming, Ma, Qiongxu, Chen, Linxun, Han, Bing, Yan, Junchi
Publikováno v:
Proceedings of the ACM Web Conference 2024 (WWW '24)
Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CDSR works design their elaborate structures relying on overlapping user
Externí odkaz:
http://arxiv.org/abs/2311.04590
Autor:
Xu, Wujiang, Ning, Xuying, Lin, Wenfang, Ha, Mingming, Ma, Qiongxu, Liang, Qianqiao, Tao, Xuewen, Chen, Linxun, Han, Bing, Luo, Minnan
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems. The existing approaches aim to design a specific cross-domain unit that can transfer and propag
Externí odkaz:
http://arxiv.org/abs/2311.04760
Autor:
Xu, Wujiang, Li, Shaoshuai, Ha, Mingming, Guo, Xiaobo, Ma, Qiongxu, Liu, Xiaolei, Chen, Linxun, Zhu, Zhenfeng
Publikováno v:
The IEEE International Conference on Data Engineering 2023
Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of interest recently, which intends to improve the recommendation performance in multiple domains (or systems) simultaneously. Most existing multi-target CDR frameworks primarily rel
Externí odkaz:
http://arxiv.org/abs/2302.05919
Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to address the difficulty of user identification and unavailability of histor
Externí odkaz:
http://arxiv.org/abs/2210.12940
Autor:
Li, Yuyuan, Chen, Chaochao, Zheng, Xiaolin, Zhang, Yizhao, Gong, Biao, Wang, Jun, Chen, Linxun
Publikováno v:
In Expert Systems With Applications 30 December 2023 234
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.