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pro vyhledávání: '"Jin, Taiwei"'
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:
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
Deep learning-based sequential recommender systems have recently attracted increasing attention from both academia and industry. Most of industrial Embedding-Based Retrieval (EBR) system for recommendation share the similar ideas with sequential reco
Externí odkaz:
http://arxiv.org/abs/2010.12837
Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching approaches in industry. However, they are not effective to model dynamic a
Externí odkaz:
http://arxiv.org/abs/1909.00385
Autor:
Tang, Yan, Chang, Qingcheng, Chen, Gang, Zhao, Xiaomei, Huang, Gui, Wang, Tong, Jia, Changhao, Lu, Linghong, Jin, Taiwei, Yang, Shudi, Cao, Li, Zhang, Xuenong
Publikováno v:
In Biomaterials Advances July 2023 150
Akademický článek
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