Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Yang Keping"'
Autor:
Lu, Fan, Li, Qimai, Liu, Bo, Wu, Xiao-Ming, Zhang, Xiaotong, Lv, Fuyu, Lin, Guli, Li, Sen, Jin, Taiwei, Yang, Keping
User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance by jointly learning semantic representations of products, users, and te
Externí odkaz:
http://arxiv.org/abs/2202.06081
Autor:
Zhao, Wang, Cheng, Zhifeng, Ji, Xianyou, Pei, Zhaohui, Yang, Keping, Huang, Zhouqing, Wu, Yanqing, Wang, Gaopin, Wang, Minghui, Zhao, Yi, Bai, Xuelian, Zhao, Shuiping
Publikováno v:
In Nutrition, Metabolism and Cardiovascular Diseases September 2024 34(9):2217-2225
Autor:
Sun, Yunfeng, Wu, Yawei, Jing, Ronghua, Yang, Keping, Wang, Xiaoya, Zhao, Xiaoxiao, Fede, Caterina, Stecco, Carla
Publikováno v:
In Heliyon 30 July 2024 10(14)
Autor:
Li, Sen, Lv, Fuyu, Jin, Taiwei, Lin, Guli, Yang, Keping, Zeng, Xiaoyi, Wu, Xiao-Ming, Ma, Qianli
Nowadays, the product search service of e-commerce platforms has become a vital shopping channel in people's life. The retrieval phase of products determines the search system's quality and gradually attracts researchers' attention. Retrieving the mo
Externí odkaz:
http://arxiv.org/abs/2106.09297
Autor:
Chen, Jiawei, Dong, Hande, Qiu, Yang, He, Xiangnan, Xin, Xin, Chen, Liang, Lin, Guli, Yang, Keping
Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causing various biases in the data which significantly affect the learned mo
Externí odkaz:
http://arxiv.org/abs/2105.04170
The search engine plays a fundamental role in online e-commerce systems, to help users find the products they want from the massive product collections. Relevance is an essential requirement for e-commerce search, since showing products that do not m
Externí odkaz:
http://arxiv.org/abs/2102.07098
Combining graph representation learning with multi-view data (side information) for recommendation is a trend in industry. Most existing methods can be categorized as \emph{multi-view representation fusion}; they first build one graph and then integr
Externí odkaz:
http://arxiv.org/abs/2005.10110
Autor:
Luo, Xusheng, Liu, Luxin, Yang, Yonghua, Bo, Le, Cao, Yuanpeng, Wu, Jinhang, Li, Qiang, Yang, Keping, Zhu, Kenny Q.
One of the ultimate goals of e-commerce platforms is to satisfy various shopping needs for their customers. Much efforts are devoted to creating taxonomies or ontologies in e-commerce towards this goal. However, user needs in e-commerce are still not
Externí odkaz:
http://arxiv.org/abs/2003.13230
Graph Convolutional Networks (GCNs) have gained significant developments in representation learning on graphs. However, current GCNs suffer from two common challenges: 1) GCNs are only effective with shallow structures; stacking multiple GCN layers w
Externí odkaz:
http://arxiv.org/abs/1912.05977
Autor:
Zhang, Wenhao, Bao, Wentian, Liu, Xiao-Yang, Yang, Keping, Lin, Quan, Wen, Hong, Ramezani, Ramin
Post-click conversion rate (CVR) estimation is a critical task in e-commerce recommender systems. This task is deemed quite challenging under the industrial setting with two major issues: 1) selection bias caused by user self-selection, and 2) data s
Externí odkaz:
http://arxiv.org/abs/1910.09337