Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Yongfeng Zhang"'
Publikováno v:
ACM Transactions on Information Systems. 41:1-26
Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system's ease of use, and gain users' trust. A typical approach to realize it is natural language generation.
Autor:
Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang
Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::552f43619658469b808e1005bab085f1
http://arxiv.org/abs/2307.00165
http://arxiv.org/abs/2307.00165
Autor:
Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Peng Jiang, Kun Gai, Xiangyu Zhao, Yongfeng Zhang
In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction. However, the action space of the recommendation policy is a list of it
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a169657ef4186e79b24c16d658d73634
http://arxiv.org/abs/2302.03431
http://arxiv.org/abs/2302.03431
Autor:
Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, Yongfeng Zhang
Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem--identifying the underlying reason of model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::273dd31e6752490d8a3f51af89266a89
http://arxiv.org/abs/2204.11159
http://arxiv.org/abs/2204.11159
Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender systems in online technologies, researchers have carried out several ro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::222758499b3b05ca219827c2fcdb2d1c
http://arxiv.org/abs/2201.04399
http://arxiv.org/abs/2201.04399
Autor:
Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, Yongfeng Zhang
The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algorithmic approaches mainly aim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::912145c326a57d2eb1d3550d21dd7aea
http://arxiv.org/abs/2201.00140
http://arxiv.org/abs/2201.00140
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering. However, most of the graph-structured data in practice suffers from inco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c4db6cc5506cedd31ddbfbe080276c4
http://arxiv.org/abs/2112.13705
http://arxiv.org/abs/2112.13705
Autor:
Xu Chen, Yongfeng Zhang
Publikováno v:
Foundations and Trends® in Information Retrieval. 14:1-101
Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable o
Publikováno v:
SIGIR
Recently, research on explainable recommender systems has drawn much attention from both academia and industry, resulting in a variety of explainable models. As a consequence, their evaluation approaches vary from model to model, which makes it quite
Publikováno v:
WWW
Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations. In order to deal with the enormous combinatorial space of slates, recent w