activity
20212025
most citedM2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations

22 citations · 36 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2025

LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation

Xinyuan Wang, Haoyue Bai, Nanxu Gong +4

Feature transformation enhances data representation by deriving new features from the original data. Generative AI offers potential for this task, but faces challenges in stable ge…

cs.LG2023★ 4 cited

Local Boosting for Weakly-Supervised Learning

Rongzhi Zhang, Yue Yu, Jiaming Shen +2

Boosting is a commonly used technique to enhance the performance of a set of base models by combining them into a strong ensemble model. Though widely adopted, boosting is typicall…

cs.IR2022★ 22 cited

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

Walid Shalaby, Sejoon Oh, Amir Afsharinejad +2

Session-based recommender systems (SBRSs) have shown superior performance over conventional methods. However, they show limited scalability on large-scale industrial datasets since…

cs.LG2022★ 5 cited

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

Rongzhi Zhang, Rebecca West, Xiquan Cui +1

On e-commerce platforms, predicting if two products are compatible with each other is an important functionality to achieve trustworthy product recommendation and search experience…

cs.IR2021★ 3 cited

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

Ding Xiang, Becky West, Jiaqi Wang +2

E-commerce sites strive to provide users the most timely relevant information in order to reduce shopping frictions and increase customer satisfaction. Multi armed bandit models (M…

cs.IR2021★ 1 cited

Online Product Feature Recommendations with Interpretable Machine Learning

Mingming Guo, Nian Yan, Xiquan Cui +2

Product feature recommendations are critical for online customers to purchase the right products based on the right features. For a customer, selecting the product that has the bes…

cs.IR2021

Deep Learning-based Online Alternative Product Recommendations at Scale

Mingming Guo, Nian Yan, Xiquan Cui +4

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 amon…

cs.AI2021★ 1 cited

Interpretable Methods for Identifying Product Variants

Rebecca West, Khalifeh Al Jadda, Unaiza Ahsan +2

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 a…