192 citations · 338 across the 27 of their papers we have counts for
6 papers · 1 filter
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…
Towards Fair Recommendation in Two-Sided Platforms
Arpita Biswas, Gourab K Patro, Niloy Ganguly +2
Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Trad…
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…
Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrieval
Ashmi Banerjee, Gourab K Patro, Linus W. Dietz +1
The proliferation of smartphones has led to the increased popularity of location-based search and recommendation systems. Online platforms like Google and Yelp allow location-based…
Public Sphere 2.0: Targeted Commenting in Online News Media
Ankan Mullick, Sayan Ghosh, Ritam Dutt +2
With the increase in online news consumption, to maximize advertisement revenue, news media websites try to attract and retain their readers on their sites. One of the most effecti…
Summarizing User-generated Textual Content: Motivation and Methods for Fairness in Algorithmic Summaries
Abhisek Dash, Anurag Shandilya, Arindam Biswas +3
As the amount of user-generated textual content grows rapidly, text summarization algorithms are increasingly being used to provide users a quick overview of the information conten…