Demo Data from SIGIR 2026 / reviewers in the wild / expert
Kenny Powar
dblp:228/1371
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
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% | |
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Recommender systems › social recommendation › social media recommendation
influencer recommendation |
0.3 | 1 | 2018 | SoMin.ai: Social Multimedia Influencer Discovery Marketplace · ACM Multimedia 2018 |
Multimedia analysis and retrieval › social media analysis
social multimedia analysis |
0.1 | 1 | 2018 | SoMin.ai: Social Multimedia Influencer Discovery Marketplace · ACM Multimedia 2018 |
Methods — techniques the papers use, named apart from their topics
user profiling · 0.7semantic search · 0.7behavioral science · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | SoMin.ai: Social Multimedia Influencer Discovery MarketplaceabstractIn this technical demonstration, we showcase the first ai-driven social multimedia influencer discovery marketplace, called SoMin. The platform combines advanced data analytics and behavioral science to help marketers find, understand their audience and engage the most relevant social media micro-influencers at a large scale. SoMin harvests brand-specific life social multimedia streams in a specified market domain, followed by rich analytics and semantic-based influencer search. The Individual User Profiling models extrapolate the key personal characteristics of the brand audience, while the influencer retrieval engine reveals the semantically-matching social media influencers to the platform users. The influencers are matched in terms of both their-posted content and social media audiences, while the evaluation results demonstrate an excellent performance of the proposed recommender framework. By leveraging influencers at a large scale, marketers will be able to execute more effective marketing campaigns of higher trust and at a lower cost. Aleksandr Farseev, Kirill Lepikhin, Hendrik Schwartz, Eu Khoon Ang, Kenny Powar |
ACM Multimedia | 5 |