Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Luo Hengliang"'
Large Language Models (LLMs) have recently garnered significant attention in various domains, including recommendation systems. Recent research leverages the capabilities of LLMs to improve the performance and user modeling aspects of recommender sys
Externí odkaz:
http://arxiv.org/abs/2411.02041
Autor:
Lan, Xiaochong, Gao, Chen, Wen, Shiqi, Chen, Xiuqi, Che, Yingge, Zhang, Han, Wei, Huazhou, Luo, Hengliang, Li, Yong
Living needs refer to the various needs in human's daily lives for survival and well-being, including food, housing, entertainment, etc. On life service platforms that connect users to service providers, such as Meituan, the problem of living needs p
Externí odkaz:
http://arxiv.org/abs/2307.16644
Autor:
Tao, Xian, Qu, Zhen, Luo, Hengliang, Han, Jianwen, He, Yonghao, Liu, Danfeng, Lv, Chengkan, Shen, Fei, Zhang, Zhengtao
The Vision Challenge Track 1 for Data-Effificient Defect Detection requires competitors to instance segment 14 industrial inspection datasets in a data-defificient setting. This report introduces the technical details of the team Aoi-overfifitting-Te
Externí odkaz:
http://arxiv.org/abs/2306.14116
Autor:
Ren, Zhaochun, Huang, Na, Wang, Yidan, Ren, Pengjie, Ma, Jun, Lei, Jiahuan, Shi, Xinlei, Luo, Hengliang, Jose, Joemon M, Xin, Xin
Learning reinforcement learning (RL)-based recommenders from historical user-item interaction sequences is vital to generate high-reward recommendations and improve long-term cumulative benefits. However, existing RL recommendation methods encounter
Externí odkaz:
http://arxiv.org/abs/2305.11081
Autor:
Xin, Xin, Liu, Xiangyuan, Wang, Hanbing, Ren, Pengjie, Chen, Zhumin, Lei, Jiahuan, Shi, Xinlei, Luo, Hengliang, Jose, Joemon, de Rijke, Maarten, Ren, Zhaochun
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases. Using multiple types of implicit user fee
Externí odkaz:
http://arxiv.org/abs/2305.05585
Autor:
Xin, Xin, Yang, Jiyuan, Wang, Hanbing, Ma, Jun, Ren, Pengjie, Luo, Hengliang, Shi, Xinlei, Chen, Zhumin, Ren, Zhaochun
Modern recommender systems are trained to predict users potential future interactions from users historical behavior data. During the interaction process, despite the data coming from the user side recommender systems also generate exposure data to p
Externí odkaz:
http://arxiv.org/abs/2210.08435
Autor:
Wang, Zihan, Huang, Na, Sun, Fei, Ren, Pengjie, Chen, Zhumin, Luo, Hengliang, de Rijke, Maarten, Ren, Zhaochun
Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems through the lens of membership inference. In such attacks, an adversa
Externí odkaz:
http://arxiv.org/abs/2206.12401
Autor:
Gu, Jinjin, Cai, Haoming, Dong, Chao, Ren, Jimmy S., Qiao, Yu, Gu, Shuhang, Timofte, Radu, Cheon, Manri, Yoon, Sungjun, Kang, Byungyeon, Lee, Junwoo, Zhang, Qing, Guo, Haiyang, Bin, Yi, Hou, Yuqing, Luo, Hengliang, Guo, Jingyu, Wang, Zirui, Wang, Hai, Yang, Wenming, Bai, Qingyan, Shi, Shuwei, Xia, Weihao, Cao, Mingdeng, Wang, Jiahao, Chen, Yifan, Yang, Yujiu, Li, Yang, Zhang, Tao, Feng, Longtao, Liao, Yiting, Li, Junlin, Thong, William, Pereira, Jose Costa, Leonardis, Ales, McDonagh, Steven, Xu, Kele, Yang, Lehan, Cai, Hengxing, Sun, Pengfei, Ayyoubzadeh, Seyed Mehdi, Royat, Ali, Fezza, Sid Ahmed, Hammou, Dounia, Hamidouche, Wassim, Ahn, Sewoong, Yoon, Gwangjin, Tsubota, Koki, Akutsu, Hiroaki, Aizawa, Kiyoharu
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image processing techno
Externí odkaz:
http://arxiv.org/abs/2105.03072
Akademický článek
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Publikováno v:
RecSys
The diversity of recommendation has attracted a lot of attention in recommender systems due to its ability to improve user experience. Most of the diversified recommendation tasks usually exploit user-item interaction records for mining user explicit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3f11eb03a825cd627a1cf74d578318e
https://doi.org/10.1145/3460231.3478845
https://doi.org/10.1145/3460231.3478845
