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pro vyhledávání: '"Lin, Kangyi"'
The rise of online multi-modal sharing platforms like TikTok and YouTube has enabled personalized recommender systems to incorporate multiple modalities (such as visual, textual, and acoustic) into user representations. However, addressing the challe
Externí odkaz:
http://arxiv.org/abs/2406.11781
GNN-based recommenders have excelled in modeling intricate user-item interactions through multi-hop message passing. However, existing methods often overlook the dynamic nature of evolving user-item interactions, which impedes the adaption to changin
Externí odkaz:
http://arxiv.org/abs/2311.16716
Current sequential recommender systems are proposed to tackle the dynamic user preference learning with various neural techniques, such as Transformer and Graph Neural Networks (GNNs). However, inference from the highly sparse user behavior data may
Externí odkaz:
http://arxiv.org/abs/2303.11780
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention in recommendation and benefited
Externí odkaz:
http://arxiv.org/abs/2303.07797
With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely increases, which limits the use of high-order interaction models in real i
Externí odkaz:
http://arxiv.org/abs/2211.11159
Click-Through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge, which either perform few-shot user representation learning or adopt op
Externí odkaz:
http://arxiv.org/abs/2210.16080
Autor:
Min, Erxue, Rong, Yu, Xu, Tingyang, Bian, Yatao, Zhao, Peilin, Huang, Junzhou, Luo, Da, Lin, Kangyi, Ananiadou, Sophia
Click-Through Rate (CTR) prediction, which aims to estimate the probability that a user will click an item, is an essential component of online advertising. Existing methods mainly attempt to mine user interests from users' historical behaviours, whi
Externí odkaz:
http://arxiv.org/abs/2201.13311
Autor:
Zhuo, Xingrui1 zxr@mail.hfut.edu.cn, Qian, Shengsheng2 shengsheng.qian@nlpr.ia.ac.cn, Hu, Jun3 hujunxianligong@gmail.com, Dai, Fuxin4 fuxindai@tencent.com, Lin, Kangyi4 plancklin@tencent.com, Wu, Gongqing1 wugq@hfut.edu.cn
Publikováno v:
ACM Transactions on Information Systems. Nov2024, Vol. 42 Issue 6, p1-28. 28p.
Publikováno v:
ACM Transactions on Information Systems. 41:1-26
Click-Through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge, which either perform few-shot user representation learning or adopt op
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c411e2ec599af0375e6cd24bac47a8f8
https://doi.org/10.1145/3564283
https://doi.org/10.1145/3564283
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