Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Shreyans J. Nagori

dblp:345/6401 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-0734-9550ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Recommender systems · 100%
Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Recommender systems
fairness-aware recommendation
0.712023
Towards Fair Allocation in Social Commerce Platforms · WWW 2023
Algorithmic game theory and mechanism design
fair division
0.712023
Towards Fair Allocation in Social Commerce Platforms · WWW 2023
Computational social science and digital humanities › marketing
social commerce
0.212023
Towards Fair Allocation in Social Commerce Platforms · WWW 2023

Methods — techniques the papers use, named apart from their topics

fair division · 2.0
YearPublicationVenuePosition
2023 Towards Fair Allocation in Social Commerce Platforms
abstract
Social commerce platforms are emerging businesses where producers sell products through re-sellers who advertise the products to other customers in their social network. Due to the increasing popularity of this business model, thousands of small producers and re-sellers are starting to depend on these platforms for their livelihood; thus, it is important to provide fair earning opportunities to them. The enormous product space in such platforms prohibits manual search, and motivates the need for recommendation algorithms to effectively allocate product exposure and, consequently, earning opportunities. In this work, we focus on the fairness of such allocations in social commerce platforms and formulate the problem of assigning products to re-sellers as a fair division problem with indivisible items under two-sided cardinality constraints, wherein each product must be given to at least a certain number of re-sellers and each re-seller must get a certain number of products.
Shreyans J. Nagori, Abhijnan Chakraborty, Rohit Vaish, Sayan Ranu, Prajit Prashant Sinai Nadkarni, Narendra Varma Dasararaju, Muthusamy Chelliah
WWW2