72 citations · 213 across the 23 of their papers we have counts for
10 papers · 1 filter
GAN-based Recommendation with Positive-Unlabeled Sampling
Yao Zhou, Jianpeng Xu, Jun Wu +4
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products. In this work, we propose a Generative Advers…
A Real-Time Whole Page Personalization Framework for E-Commerce
Aditya Mantha, Anirudha Sundaresan, Shashank Kedia +6
E-commerce platforms consistently aim to provide personalized recommendations to drive user engagement, enhance overall user experience, and improve business metrics. Most e-commer…
On Detecting Data Pollution Attacks On Recommender Systems Using Sequential GANs
Behzad Shahrasbi, Venugopal Mani, Apoorv Reddy Arrabothu +3
Recommender systems are an essential part of any e-commerce platform. Recommendations are typically generated by aggregating large amounts of user data. A malicious actor may be mo…
On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering
Venugopal Mani, Ramasubramanian Balasubramanian, Sushant Kumar +2
Recommender Systems have become an integral part of online e-Commerce platforms, driving customer engagement and revenue. Most popular recommender systems attempt to learn from use…
Adversarial Counterfactual Learning and Evaluation for Recommender System
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
The feedback data of recommender systems are often subject to what was exposed to the users; however, most learning and evaluation methods do not account for the underlying exposur…
An End-to-End ML System for Personalized Conversational Voice Models in Walmart E-Commerce
Rahul Radhakrishnan Iyer, Praveenkumar Kanumala, Stephen Guo +1
Searching for and making decisions about products is becoming increasingly easier in the e-commerce space, thanks to the evolution of recommender systems. Personalization and recom…
Basket Recommendation with Multi-Intent Translation Graph Neural Network
Zhiwei Liu, Xiaohan Li, Ziwei Fan +3
The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket. Existing methods solve this problem by assuming the items within the same b…
Product Title Generation for Conversational Systems using BERT
Mansi Ranjit Mane, Shashank Kedia, Aditya Mantha +2
Through recent advancements in speech technology and introduction of smart devices, such as Amazon Alexa and Google Home, increasing number of users are interacting with applicatio…
Inductive Representation Learning on Temporal Graphs
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
Inductive representation learning on temporal graphs is an important step toward salable machine learning on real-world dynamic networks. The evolving nature of temporal dynamic gr…
BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network
Zhiwei Liu, Mengting Wan, Stephen Guo +2
Within-basket recommendation reduces the exploration time of users, where the user's intention of the basket matters. The intent of a shopping basket can be retrieved from both use…