Zobrazeno 1 - 10
of 148
pro vyhledávání: '"WAYNE XIN ZHAO"'
Publikováno v:
AI Open, Vol 4, Iss , Pp 154-164 (2023)
Conversational recommender system (CRS) interacts with users through multi-turn dialogues in natural language, which aims to provide high-quality recommendations for user’s instant information need. Although great efforts have been made to develop
Externí odkaz:
https://doaj.org/article/d373da8a03d04b329426c2f7961a56d3
Publikováno v:
ACM Transactions on Information Systems. 41:1-27
Sequential recommendation models aim to predict the interested items to a user based on his historical behaviors. To train sequential recommenders, implicit feedback data is widely adopted since it is easier to obtain than explicit feedback data. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::397178ce551ee090c58413c092a54720
https://doi.org/10.1145/3582495
https://doi.org/10.1145/3582495
Autor:
Xu Chen, Zhenlei Wang, Hongteng Xu, Jingsen Zhang, Yongfeng Zhang, Wayne Xin Zhao, Ji-Rong Wen
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::044edcbb7461bebe6c3d05e946ea3903
https://doi.org/10.1109/tkde.2022.3222070
https://doi.org/10.1109/tkde.2022.3222070
Publikováno v:
ACM Transactions on Information Systems. 41:1-41
In recommender systems, top- N recommendation is an important task with implicit feedback data. Although the recent success of deep learning largely pushes forward the research on top- N recommendation, there are increasing concerns on appropriate ev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b927a3123c9c8958347bd64b37f6e58
https://doi.org/10.1145/3545796
https://doi.org/10.1145/3545796
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031247545
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2574dfe8a6b5563b86843223db674493
https://doi.org/10.1007/978-3-031-24755-2_2
https://doi.org/10.1007/978-3-031-24755-2_2
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783031306716
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe1daefc8f717795f98581b1fdecd1ea
https://doi.org/10.1007/978-3-031-30672-3_23
https://doi.org/10.1007/978-3-031-30672-3_23
Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models~(PLM) to encode item text into item representations. Despite the promising trans
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1efb78b18c3016a3c49e3ac5eea917f7
http://arxiv.org/abs/2210.12316
http://arxiv.org/abs/2210.12316
Autor:
Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, Yushuo Chen, Lanling Xu, Gaowei Zhang, Zhen Tian, Changxin Tian, Shanlei Mu, Xinyan Fan, Xu Chen, Ji-Rong Wen
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures. First of all, from a data perspective, we consider t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa581210304c1a5c48f5d09c4252cf9d
https://doi.org/10.1145/3511808.3557680
https://doi.org/10.1145/3511808.3557680
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::26fcd47270b97ca3160f82d480996e02
https://doi.org/10.1145/3511808.3557440
https://doi.org/10.1145/3511808.3557440
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e151dccbbe59b486f1a746616578be68
https://doi.org/10.1145/3511808.3557071
https://doi.org/10.1145/3511808.3557071