activity
20172022
most citedStance Detection in Web and Social Media: A Comparative Study

49 citations · 98 across the 15 of their papers we have counts for

collaborators

19 papers

cs.IR2022

FaiRIR: Mitigating Exposure Bias from Related Item Recommendations in Two-Sided Platforms

Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh +2

Related Item Recommendations (RIRs) are ubiquitous in most online platforms today, including e-commerce and content streaming sites. These recommendations not only help users compa…

cs.HC2022

Alexa, in you, I trust! Fairness and Interpretability Issues in E-commerce Search through Smart Speakers

Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh +2

In traditional (desktop) e-commerce search, a customer issues a specific query and the system returns a ranked list of products in order of relevance to the query. An increasingly…

cs.CL2021

Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents

Paheli Bhattacharya, Soham Poddar, Koustav Rudra +2

Automatic summarization of legal case documents is an important and practical challenge. Apart from many domain-independent text summarization algorithms that can be used for this…

cs.CY20214 cited

When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces

Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh +2

Algorithmic recommendations mediate interactions between millions of customers and products (in turn, their producers and sellers) on large e-commerce marketplaces like Amazon. In…

cs.IR2021

Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarization

Anurag Shandilya, Abhisek Dash, Abhijnan Chakraborty +2

With the surge in user-generated textual information, there has been a recent increase in the use of summarization algorithms for providing an overview of the extensive content. Tr…

cs.IR2021

An Unsupervised Normalization Algorithm for Noisy Text: A Case Study for Information Retrieval and Stance Detection

Anurag Roy, Shalmoli Ghosh, Kripabandhu Ghosh +1

A large fraction of textual data available today contains various types of 'noise', such as OCR noise in digitized documents, noise due to informal writing style of users on microb…