Search Results for author: Abhijnan Chakraborty

Found 8 papers, 2 papers with code

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

no code implementations30 Jan 2021 Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi

Along a number of our proposed bias measures, we find that the sponsored recommendations are significantly more biased toward Amazon private label products compared to organic recommendations.

Fairness

Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrieval

no code implementations14 Nov 2020 Ashmi Banerjee, Gourab K Patro, Linus W. Dietz, Abhijnan Chakraborty

Online platforms like Google and Yelp allow location-based search in the form of nearby feature to query for hotels or restaurants in the vicinity.

Recommendation Systems

On Fair Virtual Conference Scheduling: Achieving Equitable Participant and Speaker Satisfaction

no code implementations24 Oct 2020 Gourab K Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna P. Gummadi

We show that the welfare and fairness objectives can be in conflict with each other, and there is a need to maintain a balance between these objective while caring for them simultaneously.

Fairness

FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

1 code implementation25 Feb 2020 Gourab K Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty

We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other.

Fairness

On the Fairness of Time-Critical Influence Maximization in Social Networks

no code implementations16 May 2019 Junaid Ali, Mahmoudreza Babaei, Abhijnan Chakraborty, Baharan Mirzasoleiman, Krishna P. Gummadi, Adish Singla

As we show in this paper, the time-criticality of the information could further exacerbate the disparity of influence across groups.

Social and Information Networks Computers and Society

Public Sphere 2.0: Targeted Commenting in Online News Media

no code implementations21 Feb 2019 Ankan Mullick, Sayan Ghosh, Ritam Dutt, Avijit Ghosh, Abhijnan Chakraborty

Because the readers lack the time to go over an entire article, most of the comments are relevant to only particular sections of an article.

Summarizing User-generated Textual Content: Motivation and Methods for Fairness in Algorithmic Summaries

1 code implementation22 Oct 2018 Abhisek Dash, Anurag Shandilya, Arindam Biswas, Kripabandhu Ghosh, Saptarshi Ghosh, Abhijnan Chakraborty

Specifically, considering that an extractive summarization algorithm selects a subset of the textual units (e. g. microblogs) in the original data for inclusion in the summary, we investigate whether this selection is fair or not.

Extractive Summarization Fairness +1

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