Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Junliang, Yu"'
The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems is amplified in group recommendation because interaction data between
Externí odkaz:
http://arxiv.org/abs/2210.09672
Publikováno v:
Waike lilun yu shijian, Vol 27, Iss 03, Pp 249-252 (2022)
Objective To study myopectineal orifice anatomy of the patients with inguinal groin hernia using three-dimensional imaging technology after reconstructing of myopectineal orifice. Methods Preoperative CT images of 90 patients with inguinal hernia inc
Externí odkaz:
https://doaj.org/article/08cdece563f04c4ab685bf50d3e07c9e
Publikováno v:
IEEE Access, Vol 5, Pp 21557-21566 (2017)
Traditional recommender systems often suffer from the problem of data sparsity, because most users rate only a few of the millions of possible items. With the development of social platforms, incorporating abundant social relationships into recommend
Externí odkaz:
https://doaj.org/article/6ff7cd543fd748a58c2fbb7135bd0131
Autor:
XIN XIA1 x.xia@uq.edu.au, JUNLIANG YU1 jl.yu@uq.edu.au, QINYONG WANG2 wangqinyong@baidu.com, CHAOQUN YANG3 chaoqun.yang@griffith.edu.au, NGUYEN QUOC VIET HUNG3 henry.nguyen@griffith.edu.au, HONGZHI YIN1 db.hongzhi@gmail.com
Publikováno v:
ACM Transactions on Information Systems. Oct2023, Vol. 41 Issue 4, p1-24. 24p.
Publikováno v:
Min Gao
Heterogeneous Information Network (HIN) is a natural and general representation of data in recommender systems. Combining HIN and recommender systems can not only help model user behaviors but also make the recommendation results explainable by align
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::334ef20fc5b7db400b199ff15159bc20
https://doi.org/10.1016/j.neucom.2021.07.038
https://doi.org/10.1016/j.neucom.2021.07.038
On-device session-based recommendation systems have been achieving increasing attention on account of the low energy/resource consumption and privacy protection while providing promising recommendation performance. To fit the powerful neural session-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5faa7125299e23a0e9bbace10cf07198
http://arxiv.org/abs/2209.13422
http://arxiv.org/abs/2209.13422
Publikováno v:
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8850bf439c9fe4ee45322eb19f13cdf4
https://doi.org/10.1145/3477495.3531937
https://doi.org/10.1145/3477495.3531937
Publikováno v:
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user requests are required, which comes at the cost of a huge carbon footpr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f348dd381b7cb22f0a082a5608ed4326
https://doi.org/10.1145/3477495.3531775
https://doi.org/10.1145/3477495.3531775
Autor:
Riccardo Tommasini, Senjuti Basu Roy, Xuan Wang, Hongwei Wang, Heng Ji, Jiawei Han, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Markus Schedl, Elisabeth Lex, Akash Bharadwaj, Graham Cormode, Milan Dojchinovski, Jan Forberg, Johannes Frey, Pieter Bonte, Marco Balduini, Matteo Belcao, Emanuele Della Valle, Junliang Yu, Hongzhi Yin, Tong Chen, Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Jamell Dacon, Lingjuan Lye, Jiliang Tang, Aristides Gionis, Stefan Neumann, Bruno Ordozgoiti, Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian Suchanek, Lingfei Wu, Yu Chen, Yunyao Li, Bang Liu, Filip Ilievski, Daniel Garijo, Hans Chalupsky, Pedro Szekely, Ilias Kanellos, Dimitris Sacharidis, Thanasis Vergoulis, Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan Sengamedu, Chandan K. Reddy, Friedhelm Victor, Bernhard Haslhofer, George Katsogiannis- Meimarakis, Georgia Koutrika, Shengmin Jin, Danai Koutra, Reza Zafarani, Yulia Tsvetkov, Vidhisha Balachandran, Sachin Kumar, Xiangyu Zhao, Bo Chen, Huifeng Guo, Yejing Wang, Ruiming Tang, Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, Xiangnan He
This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% of these are hands on. Published version
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::646b27f2a0da768c4c9127736ec92e15
https://hdl.handle.net/10919/112211
https://hdl.handle.net/10919/112211
Self-supervised learning (SSL) recently has achieved outstanding success on recommendation. By setting up an auxiliary task (either predictive or contrastive), SSL can discover supervisory signals from the raw data without human annotation, which gre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b016165142d2012a7c6b36322f2db64
http://arxiv.org/abs/2203.03982
http://arxiv.org/abs/2203.03982