Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Zhou Guorui"'
Autor:
Luo, Xinchen, Cao, Jiangxia, Sun, Tianyu, Yu, Jinkai, Huang, Rui, Yuan, Wei, Lin, Hezheng, Zheng, Yichen, Wang, Shiyao, Hu, Qigen, Qiu, Changqing, Zhang, Jiaqi, Zhang, Xu, Yan, Zhiheng, Zhang, Jingming, Zhang, Simin, Wen, Mingxing, Liu, Zhaojie, Gai, Kun, Zhou, Guorui
In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeling. In industry, a wide-used modeling architecture is a cascading para
Externí odkaz:
http://arxiv.org/abs/2411.11739
In large-scale content recommendation systems, retrieval serves as the initial stage in the pipeline, responsible for selecting thousands of candidate items from billions of options to pass on to ranking modules. Traditionally, the dominant retrieval
Externí odkaz:
http://arxiv.org/abs/2411.10057
Autor:
Lv, Xiao, Cao, Jiangxia, Guan, Shijie, Zhou, Xiaoyou, Qi, Zhiguang, Zang, Yaqiang, Li, Ming, Wang, Ben, Gai, Kun, Zhou, Guorui
Scaling-law has guided the language model designing for past years, however, it is worth noting that the scaling laws of NLP cannot be directly applied to RecSys due to the following reasons: (1) The amount of training samples and model parameters is
Externí odkaz:
http://arxiv.org/abs/2411.09425
Autor:
Liu, Qi, Zheng, Kai, Huang, Rui, Li, Wuchao, Cai, Kuo, Chai, Yuan, Niu, Yanan, Hui, Yiqun, Han, Bing, Mou, Na, Wang, Hongning, Bao, Wentian, Yu, Yunen, Zhou, Guorui, Li, Han, Song, Yang, Lian, Defu, Gai, Kun
Industrial recommendation systems (RS) rely on the multi-stage pipeline to balance effectiveness and efficiency when delivering items from a vast corpus to users. Existing RS benchmark datasets primarily focus on the exposure space, where novel RS al
Externí odkaz:
http://arxiv.org/abs/2410.20868
In addressing the persistent challenges of data-sparsity and cold-start issues in domain-expert recommender systems, Cross-Domain Recommendation (CDR) emerges as a promising methodology. CDR aims at enhancing prediction performance in the target doma
Externí odkaz:
http://arxiv.org/abs/2409.04540
Autor:
Li, Wuchao, Huang, Rui, Zhao, Haijun, Liu, Chi, Zheng, Kai, Liu, Qi, Mou, Na, Zhou, Guorui, Lian, Defu, Song, Yang, Bao, Wentian, Yu, Enyun, Ou, Wenwu
Sequential Recommendation (SR) plays a pivotal role in recommender systems by tailoring recommendations to user preferences based on their non-stationary historical interactions. Achieving high-quality performance in SR requires attention to both ite
Externí odkaz:
http://arxiv.org/abs/2408.12153
Autor:
Deng, Jiaxin, Wang, Shiyao, Lu, Song, Li, Yinfeng, Luo, Xinchen, Liu, Yuanjun, Xu, Peixing, Zhou, Guorui
State-of-the-art sequential recommendation models heavily rely on transformer's attention mechanism. However, the quadratic computational and memory complexities of self attention have limited its scalability for modeling users' long range behaviour
Externí odkaz:
http://arxiv.org/abs/2408.09380
Autor:
Cao, Jiangxia, Wang, Shen, Li, Yue, Wang, Shenghui, Tang, Jian, Wang, Shiyao, Yang, Shuang, Liu, Zhaojie, Zhou, Guorui
Kuaishou, is one of the largest short-video and live-streaming platform, compared with short-video recommendations, live-streaming recommendation is more complex because of: (1) temporarily-alive to distribution, (2) user may watch for a long time wi
Externí odkaz:
http://arxiv.org/abs/2408.05709
In this paper, we present the practical problems and the lessons learned at short-video services from Kuaishou. In industry, a widely-used multi-task framework is the Mixture-of-Experts (MoE) paradigm, which always introduces some shared and specific
Externí odkaz:
http://arxiv.org/abs/2408.05430
Recently, live streaming platforms have gained immense popularity. Traditional video highlight detection mainly focuses on visual features and utilizes both past and future content for prediction. However, live streaming requires models to infer with
Externí odkaz:
http://arxiv.org/abs/2407.12002