Zobrazeno 1 - 10
of 890
pro vyhledávání: '"LIU, Zhongyi"'
Autor:
Wu, Kaixin, Ji, Yixin, Chen, Zeyuan, Wang, Qiang, Wang, Cunxiang, Liu, Hong, Ji, Baijun, Xu, Jia, Liu, Zhongyi, Gu, Jinjie, Zhou, Yuan, Mo, Linjian
Relevance modeling between queries and items stands as a pivotal component in commercial search engines, directly affecting the user experience. Given the remarkable achievements of large language models (LLMs) in various natural language processing
Externí odkaz:
http://arxiv.org/abs/2412.01269
Autor:
Chen, Zeyuan, Wu, Haiyan, Wu, Kaixin, Chen, Wei, Zhong, Mingjie, Xu, Jia, Liu, Zhongyi, Zhang, Wei
Relevance modeling is a critical component for enhancing user experience in search engines, with the primary objective of identifying items that align with users' queries. Traditional models only rely on the semantic congruence between queries and it
Externí odkaz:
http://arxiv.org/abs/2408.09439
Autor:
Shen, Kaiming, Ding, Xichen, Zheng, Zixiang, Gong, Yuqi, Li, Qianqian, Liu, Zhongyi, Zhang, Guannan
The modeling of users' behaviors is crucial in modern recommendation systems. A lot of research focuses on modeling users' lifelong sequences, which can be extremely long and sometimes exceed thousands of items. These models use the target item to se
Externí odkaz:
http://arxiv.org/abs/2407.10714
In recent years, large language models (LLMs) have driven advances in natural language processing. Still, their growing scale has increased the computational burden, necessitating a balance between efficiency and performance. Low-rank compression, a
Externí odkaz:
http://arxiv.org/abs/2405.10616
Autor:
Li, Mingzhe, Chen, Xiuying, Xiang, Jing, Zhang, Qishen, Ma, Changsheng, Dai, Chenchen, Chang, Jinxiong, Liu, Zhongyi, Zhang, Guannan
Text matching systems have become a fundamental service in most searching platforms. For instance, they are responsible for matching user queries to relevant candidate items, or rewriting the user-input query to a pre-selected high-performing one for
Externí odkaz:
http://arxiv.org/abs/2402.07788
Autor:
Sun, Xiaojie, Bi, Keping, Guo, Jiafeng, Yang, Sihui, Zhang, Qishen, Liu, Zhongyi, Zhang, Guannan, Cheng, Xueqi
Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e.g., products with aspects such as category and brand. Recent work has proposed two approaches to incorp
Externí odkaz:
http://arxiv.org/abs/2312.02538
Autor:
Jiang, Chen, Liu, Hong, Yu, Xuzheng, Wang, Qing, Cheng, Yuan, Xu, Jia, Liu, Zhongyi, Guo, Qingpei, Chu, Wei, Yang, Ming, Qi, Yuan
In recent years, the explosion of web videos makes text-video retrieval increasingly essential and popular for video filtering, recommendation, and search. Text-video retrieval aims to rank relevant text/video higher than irrelevant ones. The core of
Externí odkaz:
http://arxiv.org/abs/2309.11082
In modern commercial search engines and recommendation systems, data from multiple domains is available to jointly train the multi-domain model. Traditional methods train multi-domain models in the multi-task setting, with shared parameters to learn
Externí odkaz:
http://arxiv.org/abs/2309.08939