72 citations · 213 across the 23 of their papers we have counts for
24 papers
Generating Rich Product Descriptions for Conversational E-commerce Systems
Shashank Kedia, Aditya Mantha, Sneha Gupta +2
Through recent advancements in speech technologies and introduction of smart assistants, such as Amazon Alexa, Apple Siri and Google Home, increasing number of users are interactin…
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
Xiaohan Li, Zhiwei Liu, Stephen Guo +4
Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. H…
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
The recent work by Rendle et al. (2020), based on empirical observations, argues that matrix-factorization collaborative filtering (MCF) compares favorably to neural collaborative…
Variational Inference for Category Recommendation in E-Commerce platforms
Ramasubramanian Balasubramanian, Venugopal Mani, Abhinav Mathur +2
Category recommendation for users on an e-Commerce platform is an important task as it dictates the flow of traffic through the website. It is therefore important to surface precis…
A Temporal Kernel Approach for Deep Learning with Continuous-time Information
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
Sequential deep learning models such as RNN, causal CNN and attention mechanism do not readily consume continuous-time information. Discretizing the temporal data, as we show, caus…
Theoretical Understandings of Product Embedding for E-commerce Machine Learning
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
Product embeddings have been heavily investigated in the past few years, serving as the cornerstone for a broad range of machine learning applications in e-commerce. Despite the em…
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…
Self-attention with Functional Time Representation Learning
Da Xu, Chuanwei Ruan, Sushant Kumar +2
Sequential modelling with self-attention has achieved cutting edge performances in natural language processing. With advantages in model flexibility, computation complexity and int…
Product Knowledge Graph Embedding for E-commerce
Da Xu, Chuanwei Ruan, Evren Korpeoglu +2
In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key…
A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations
Aditya Mantha, Yokila Arora, Shubham Gupta +4
With growing consumer adoption of online grocery shopping through platforms such as Amazon Fresh, Instacart, and Walmart Grocery, there is a pressing business need to provide relev…
Complementary-Similarity Learning using Quadruplet Network
Mansi Ranjit Mane, Stephen Guo, Kannan Achan
We propose a novel learning framework to answer questions such as "if a user is purchasing a shirt, what other items will (s)he need with the shirt?" Our framework learns distribut…
Knowledge-aware Complementary Product Representation Learning
Da Xu, Chuanwei Ruan, Jason Cho +3
Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, w…
Generative Graph Convolutional Network for Growing Graphs
Da Xu, Chuanwei Ruan, Kamiya Motwani +3
Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existin…
Robust Counterfactual Inferences using Feature Learning and their Applications
Abhimanyu Mitra, Kannan Achan, Sushant Kumar
In a wide variety of applications, including personalization, we want to measure the difference in outcome due to an intervention and thus have to deal with counterfactual inferenc…
A Factorized Variational Technique for Phase Unwrapping in Markov Random Fields
Kannan Achan, Brendan J. Frey, Ralf Koetter
Some types of medical and topographic imaging device produce images in which the pixel values are "phase-wrapped", i.e. measured modulus a known scalar. Phase unwrapping can be vie…