no code implementations • 17 Feb 2024 • Yu Wang, Amin Javari, Janani Balaji, Walid Shalaby, Tyler Derr, Xiquan Cui
Then, we adaptively aggregate items' neighbor information considering user intention within the learned session.
no code implementations • 12 Sep 2023 • Sejoon Oh, Walid Shalaby, Amir Afsharinejad, Xiquan Cui
However, the H-MTL framework has not been investigated in SBRSs yet.
no code implementations • 5 Jun 2023 • Rongzhi Zhang, Yue Yu, Jiaming Shen, Xiquan Cui, Chao Zhang
In this work, we show that the standard implementation of the convex combination of base learners can hardly work due to the presence of noisy labels.
no code implementations • 23 Sep 2022 • Walid Shalaby, Sejoon Oh, Amir Afsharinejad, Srijan Kumar, Xiquan Cui
Using one public and one large industrial dataset, we experimentally show that state-of-the-art SBRSs have low performance on sparse sessions with sparse items.
no code implementations • 28 Jun 2022 • Rongzhi Zhang, Rebecca West, Xiquan Cui, Chao Zhang
We develop AMRule, a multi-view rule discovery framework that can (1) adaptively and iteratively discover novel rulers that can complement the current weakly-supervised model to improve compatibility prediction; (2) discover interpretable rules from both structured attribute tables and unstructured product descriptions.
no code implementations • 30 Jul 2021 • Ding Xiang, Becky West, Jiaqi Wang, Xiquan Cui, Jinzhou Huang
Second, we compare the accumulative rewards of the three MAB algorithms with more than 1, 000 trials using actual historical A/B test datasets.
no code implementations • 28 Apr 2021 • Mingming Guo, Nian Yan, Xiquan Cui, Simon Hughes, Khalifeh Al Jadda
For a customer, selecting the product that has the best trade-off between price and functionality is a time-consuming step in an online shopping experience, and customers can be overwhelmed by the available choices.
BIG-bench Machine Learning Interpretable Machine Learning +2
no code implementations • WS 2020 • Mingming Guo, Nian Yan, Xiquan Cui, San He Wu, Unaiza Ahsan, Rebecca West, Khalifeh Al Jadda
In this paper, we use both textual product information (e. g. product titles and descriptions) and customer behavior data to recommend alternative products.
no code implementations • 12 Apr 2021 • Rebecca West, Khalifeh Al Jadda, Unaiza Ahsan, Huiming Qu, Xiquan Cui
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.