Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Xiquan Cui"'
The 2nd Workshop on Interactive and Scalable Information Retrieval Methods for eCommerce (ISIR-eCom)
Publikováno v:
Companion Proceedings of the ACM Web Conference 2023.
Publikováno v:
Companion Proceedings of the ACM Web Conference 2023.
Autor:
Xiquan Cui, Vachik Dave, Yi Su, Khalifeh Al-Jadda, Srijan Kumar, Julian McAuley, Tao Ye, Kamelia Aryafar, Mohammed Korayem
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Autor:
Unaiza Ahsan, Yuanbo Wang, Alexander Guo, Kevin D. Tynes, Tianlong Xu, Estelle Afshar, Xiquan Cui
Publikováno v:
2021 International Conference on Computational Science and Computational Intelligence (CSCI).
Autor:
Mohammad Korayem, Xiquan Cui, Vachik S. Dave, Tao Ye, Srijan Kumar, Khalifeh AlJadda, Julian McAuley, Afshar Estelle, Kamelia Aryafar
Publikováno v:
KDD
Many recommender systems deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. Recent trends suggest that recommender systems should model user intent in real
Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60b481ff9e7fe8fb8d390d778ad0ccf3
http://arxiv.org/abs/2104.07572
http://arxiv.org/abs/2104.07572
Publikováno v:
WWW (Companion Volume)
For e-commerce companies with large product selections, the organization and grouping of products in meaningful ways is important for creating great customer shopping experiences and cultivating an authoritative brand image. One important way of grou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::506757b23c496dfae0de9d5d9fbf0068
Publikováno v:
IEEE BigData
Recommendation systems have been crucial in driving revenue in e-commerce especially in online retail. Complementary item recommendation is a challenging problem within this field due to the inherent difficulty in defining how products relate to each
Publikováno v:
IEEE BigData
We address the problem of learning how compatible two products are. Assessing compatibility is challenging because the meaning of compatibility changes depending on product categories. In this study, we leverage domain experts’ knowledge to generat