Zobrazeno 1 - 10
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pro vyhledávání: '"Wu, Yiqing"'
Autor:
Zhang, Yuting, Wu, Yiqing, Han, Ruidong, Sun, Ying, Zhu, Yongchun, Li, Xiang, Lin, Wei, Zhuang, Fuzhen, An, Zhulin, Xu, Yongjun
Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively, users' interactions with items are highly driven by their unchanging i
Externí odkaz:
http://arxiv.org/abs/2407.00912
Autor:
Wu, Yiqing, Xie, Ruobing, Zhang, Zhao, Zhang, Xu, Zhuang, Fuzhen, Lin, Leyu, Kang, Zhanhui, Xu, Yongjun
The graph-based recommendation has achieved great success in recent years. However, most existing graph-based recommendations focus on capturing user preference based on positive edges/feedback, while ignoring negative edges/feedback (e.g., dislike,
Externí odkaz:
http://arxiv.org/abs/2405.15280
Autor:
Wu, Yiqing, Xie, Ruobing, Zhang, Zhao, Zhuang, Fuzhen, Zhang, Xu, Lin, Leyu, Kang, Zhanhui, Xu, Yongjun
Classical sequential recommendation models generally adopt ID embeddings to store knowledge learned from user historical behaviors and represent items. However, these unique IDs are challenging to be transferred to new domains. With the thriving of p
Externí odkaz:
http://arxiv.org/abs/2405.03562
Autor:
Zhang, Yuting, Wu, Yiqing, Le, Ran, Zhu, Yongchun, Zhuang, Fuzhen, Han, Ruidong, Li, Xiang, Lin, Wei, An, Zhulin, Xu, Yongjun
Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from traditional recommendation, takeaway recommendation faces two main challen
Externí odkaz:
http://arxiv.org/abs/2306.04370
Autor:
Wu, Yiqing, Xie, Ruobing, Zhang, Zhao, Zhu, Yongchun, Zhuang, FuZhen, Zhou, Jie, Xu, Yongjun, He, Qing
Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks. D
Externí odkaz:
http://arxiv.org/abs/2305.03995
Autor:
Wu, Yiqing1 (AUTHOR), Tu, Mixue1 (AUTHOR), Liu, Yifeng1 (AUTHOR), Zhang, Dan1,2 (AUTHOR) zhangdan@zju.edu.cn
Publikováno v:
Reproductive Biology & Endocrinology. 10/8/2024, Vol. 22 Issue 1, p1-10. 10p.
Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to more effectively and efficiently extract useful knowledge from pre-tra
Externí odkaz:
http://arxiv.org/abs/2205.09666
Autor:
Wu, Yiqing, Xie, Ruobing, Zhu, Yongchun, Zhuang, Fuzhen, Ao, Xiang, Zhang, Xu, Lin, Leyu, He, Qing
Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attrib
Externí odkaz:
http://arxiv.org/abs/2205.04682
Autor:
Wu, Yiqing, Xie, Ruobing, Zhu, Yongchun, Ao, Xiang, Chen, Xin, Zhang, Xu, Zhuang, Fuzhen, Lin, Leyu, He, Qing
Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consi
Externí odkaz:
http://arxiv.org/abs/2203.10576
Autor:
Li, Jiaze, Zhang, Xiangdong, Pang, Shuai, Wu, Yiqing, Yang, Cheng, Su, Lijuan, Liu, Jiashun, Wei, Xiaogang
Publikováno v:
In Construction and Building Materials 22 November 2024 452