collaborators

6 papers

cs.IR2026

AutoRelAnnotator: Calibrated Model Cascades for Cost-Efficient Relevance Evaluation in Sponsored Search

Md Omar Faruk Rokon, Shasvat Desai, Hong Yao +1

How can we generate high-quality relevance annotations at scale without the cost and delays of human labeling? Relevance annotations are the backbone of search ranking systems whic…

cs.IR2026

Unified Multi-Task Relevance Modeling for E-Commerce: Comparing Task Routing Architectures Across LLMs and Cross-Encoders

Md Omar Faruk Rokon, Jhalak Nilesh Acharya, Shasvat Desai +2

How can we build a single relevance model that handles six different entity pair relationship types in e commerce from query product matching to product type similarity when each t…

cs.IR2026

Scaling Dense Retrieval with LLM-Annotated Training Data: Structured Mining and Progressive Curriculum for E-Commerce Sponsored Search

Md Omar Faruk Rokon, Shasvat Desai, Jhalak Nilesh Acharya +9

How can we generate high-quality training data for dense retrieval models at production scale, without relying on click signals or manual annotation? This question is critical for…

cs.IR2026

INSPIRE: Intent-aware Neural Sponsored Product Retrieval for E-commerce

Shasvat Desai, Hong Yao, Utkarsh Porwal +1

Walmart holds the largest share of the U.S. ecommerce grocery market, where food and beverage categories generate some of the highest search traffic and, consequently, drive a subs…

cs.IR2026

Unified Supervision for Walmart's Sponsored Search Retrieval via Joint Semantic Relevance and Behavioral Engagement Modeling

Shasvat Desai, Md Omar Faruk Rokon, Jhalak Nilesh Acharya +4

Modern search systems rely on a fast first stage retriever to fetch relevant items from a massive catalog of items. Deployed search systems often use user engagement signals to sup…

cs.CV2025

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters

Shasvat Desai, Debasmita Ghose, Deep Chakraborty

Visual contrastive learning aims to learn representations by contrasting similar (positive) and dissimilar (negative) pairs of data samples. The design of these pairs significantly…