Search Results for author: Xiquan Cui

Found 9 papers, 0 papers with code

Knowledge Graph-based Session Recommendation with Adaptive Propagation

no code implementations17 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.

Recommendation Systems

Local Boosting for Weakly-Supervised Learning

no code implementations5 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.

Weakly-supervised Learning

M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations

no code implementations23 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.

Session-Based Recommendations

Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction

no code implementations28 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.

Attribute Language Modelling +1

Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce

no code implementations30 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.

Thompson Sampling

Online Product Feature Recommendations with Interpretable Machine Learning

no code implementations28 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

Deep Learning-based Online Alternative Product Recommendations at Scale

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.

Recommendation Systems

Interpretable Methods for Identifying Product Variants

no code implementations12 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.

Constrained Clustering

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