Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Gao, Jingtong"'
Autor:
Liu, Langming, Zhao, Xiangyu, Zhang, Chi, Gao, Jingtong, Wang, Wanyu, Fan, Wenqi, Wang, Yiqi, He, Ming, Liu, Zitao, Li, Qing
Transformer models have achieved remarkable success in sequential recommender systems (SRSs). However, computing the attention matrix in traditional dot-product attention mechanisms results in a quadratic complexity with sequence lengths, leading to
Externí odkaz:
http://arxiv.org/abs/2411.01537
Autor:
Gao, Jingtong, Chen, Bo, Zhao, Xiangyu, Liu, Weiwen, Li, Xiangyang, Wang, Yichao, Zhang, Zijian, Wang, Wanyu, Ye, Yuyang, Lin, Shanru, Guo, Huifeng, Tang, Ruiming
Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. Traditional reranking models have focused predominantly on accuracy, but modern applications demand consideration
Externí odkaz:
http://arxiv.org/abs/2406.12433
Autor:
Hu, Jiaxi, Gao, Jingtong, Zhao, Xiangyu, Hu, Yuehong, Liang, Yuxuan, Wang, Yiqi, He, Ming, Liu, Zitao, Yin, Hongzhi
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research. In the initial stages of multimodal sequential recommendation models, the mainstream paradigm was ID-dominant recomm
Externí odkaz:
http://arxiv.org/abs/2402.17334
Autor:
Gao, Jingtong, Chen, Bo, Zhu, Menghui, Zhao, Xiangyu, Li, Xiaopeng, Wang, Yuhao, Wang, Yichao, Guo, Huifeng, Tang, Ruiming
Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overal
Externí odkaz:
http://arxiv.org/abs/2309.02061
Autor:
Liu, Qidong, Hu, Jiaxi, Xiao, Yutian, Zhao, Xiangyu, Gao, Jingtong, Wang, Wanyu, Li, Qing, Tang, Jiliang
The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, su
Externí odkaz:
http://arxiv.org/abs/2302.03883
Autor:
Liu, Ziru, Tian, Jiejie, Cai, Qingpeng, Zhao, Xiangyu, Gao, Jingtong, Liu, Shuchang, Chen, Dayou, He, Tonghao, Zheng, Dong, Jiang, Peng, Gai, Kun
In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are pr
Externí odkaz:
http://arxiv.org/abs/2302.03328
Autor:
Fan, Wenqi, Zhao, Xiangyu, Chen, Xiao, Su, Jingran, Gao, Jingtong, Wang, Lin, Liu, Qidong, Wang, Yiqi, Xu, Han, Chen, Lei, Li, Qing
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various hum
Externí odkaz:
http://arxiv.org/abs/2209.10117
Publikováno v:
Engineering Computations, 2022, Vol. 39, Issue 6, pp. 2306-2325.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/EC-10-2021-0624
Akademický článek
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Autor:
Liu, Qidong, Hu, Jiaxi, Xiao, Yutian, Zhao, Xiangyu, Gao, Jingtong, Wang, Wanyu, Li, Qing, Tang, Jiliang
Publikováno v:
ACM Computing Surveys; Feb2025, Vol. 57 Issue 2, p1-17, 17p