Search Results for author: Sushant Kumar

Found 22 papers, 5 papers with code

Event-based Product Carousel Recommendation with Query-Click Graph

no code implementations5 Feb 2024 Luyi Ma, Nimesh Sinha, Parth Vajge, Jason HD Cho, Sushant Kumar, Kannan Achan

Product recommendations for the multiple aspects of the target event are usually generated by human curators who manually identify the aspects and select a list of aspect-related products (i. e., product carousel) for each aspect as recommendations.

Recommendation Systems

LLMs with User-defined Prompts as Generic Data Operators for Reliable Data Processing

no code implementations26 Dec 2023 Luyi Ma, Nikhil Thakurdesai, Jiao Chen, Jianpeng Xu, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Although the UDF design pattern introduces flexibility, reusability and scalability, the increasing demand on machine learning pipelines brings three new challenges to this design pattern -- not low-code, not dependency-free and not knowledge-aware.

Seller-side Outcome Fairness in Online Marketplaces

no code implementations6 Dec 2023 Zikun Ye, Reza Yousefi Maragheh, Lalitesh Morishetti, Shanu Vashishtha, Jason Cho, Kaushiki Nag, Sushant Kumar, Kannan Achan

This paper aims to investigate and achieve seller-side fairness within online marketplaces, where many sellers and their items are not sufficiently exposed to customers in an e-commerce platform.


GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation

no code implementations26 Oct 2023 Ramin Giahi, Reza Yousefi Maragheh, Nima Farrokhsiar, Jianpeng Xu, Jason Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Similar item recommendation is a critical task in the e-Commerce industry, which helps customers explore similar and relevant alternatives based on their interested products.

Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs

no code implementations17 May 2023 Jiao Chen, Luyi Ma, Xiaohan Li, Nikhil Thakurdesai, Jianpeng Xu, Jason H. D. Cho, Kaushiki Nag, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complementary or substitutable relations between products or product types, which can be utilized in recommender systems.

Prompt Engineering Recommendation Systems +1

Causal Structure Learning with Recommendation System

no code implementations19 Oct 2022 Shuyuan Xu, Da Xu, Evren Korpeoglu, Sushant Kumar, Stephen Guo, Kannan Achan, Yongfeng Zhang

Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact.

Decision Making Recommendation Systems

NEAT: A Label Noise-resistant Complementary Item Recommender System with Trustworthy Evaluation

no code implementations11 Feb 2022 Luyi Ma, Jianpeng Xu, Jason H. D. Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan

On the other hand, the model evaluation will not be trustworthy if the labels for evaluation are not reflecting the true complementary relatedness.

Recommendation Systems

Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives

no code implementations23 Oct 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

The recent work by Rendle et al. (2020), based on empirical observations, argues that matrix-factorization collaborative filtering (MCF) compares favorably to neural collaborative filtering (NCF), and conjectures the dot product's superiority over the feed-forward neural network as similarity function.

Collaborative Filtering Transductive Learning

Towards the D-Optimal Online Experiment Design for Recommender Selection

1 code implementation23 Oct 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Selecting the optimal recommender via online exploration-exploitation is catching increasing attention where the traditional A/B testing can be slow and costly, and offline evaluations are prone to the bias of history data.

Multi-Armed Bandits

Variational Inference for Category Recommendation in E-Commerce platforms

no code implementations15 Apr 2021 Ramasubramanian Balasubramanian, Venugopal Mani, Abhinav Mathur, Sushant Kumar, Kannan Achan

Category recommendation for users on an e-Commerce platform is an important task as it dictates the flow of traffic through the website.

Variational Inference

A Temporal Kernel Approach for Deep Learning with Continuous-time Information

2 code implementations ICLR 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Sequential deep learning models such as RNN, causal CNN and attention mechanism do not readily consume continuous-time information.

Density Estimation

Theoretical Understandings of Product Embedding for E-commerce Machine Learning

no code implementations24 Feb 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

The generalization performance in the downstream machine learning task is controlled by the alignment between the embeddings and the product relatedness measure.

BIG-bench Machine Learning Dimensionality Reduction +2

Adversarial Counterfactual Learning and Evaluation for Recommender System

1 code implementation NeurIPS 2020 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

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 exposure mechanism.

Causal Inference counterfactual +1

Inductive Representation Learning on Temporal Graphs

4 code implementations ICLR 2020 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.

Graph Attention Graph Embedding +3

Self-attention with Functional Time Representation Learning

2 code implementations NeurIPS 2019 Da Xu, Chuanwei Ruan, Sushant Kumar, Evren Korpeoglu, Kannan Achan

To bridge the gap between modelling time-independent and time-dependent event sequence, we introduce a functional feature map that embeds time span into high-dimensional spaces.

Representation Learning Translation

Product Knowledge Graph Embedding for E-commerce

no code implementations28 Nov 2019 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

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.

Knowledge Graph Embedding Marketing +2

Knowledge-aware Complementary Product Representation Learning

no code implementations16 Mar 2019 Da Xu, Chuanwei Ruan, Jason Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness.

Multi-Task Learning Recommendation Systems +1

Generative Graph Convolutional Network for Growing Graphs

no code implementations6 Mar 2019 Da Xu, Chuanwei Ruan, Kamiya Motwani, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Here we propose a unified generative graph convolutional network that learns node representations for all nodes adaptively in a generative model framework, by sampling graph generation sequences constructed from observed graph data.

Graph Generation Graph Reconstruction +1

Robust Counterfactual Inferences using Feature Learning and their Applications

no code implementations22 Aug 2018 Abhimanyu Mitra, Kannan Achan, Sushant Kumar

From the randomized experiment, we learn the feature representations which divide the population into subpopulations where we observe statistically significant difference in average customer feedback between those who were subjected to the intervention and those who were not, with a level of significance l, where l is a configurable parameter in our model.

counterfactual Counterfactual Inference

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