Demo Data from SIGIR 2026 / reviewers in the wild / expert
Kirill Lepikhin
dblp:228/1326
· DBLP profile ↗
5ranked-venue papers
0as first author
4since 2021 · last 2026
0009-0003-5046-1702ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 4 · 4 since 2021Artificial intelligence and machine learning · 2 · 2 since 2021Graphics, 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
5 papers |
Recommender systems · 78% Information retrieval · 22% | |
| Artificial intelligence
2 papers |
Generative modeling · 100% |
Topics — the 6 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Recommender systems › advertising
advertising recommendation |
1.1 | 2 | 2025 | Fusing Predictive and Large Language Models for Actionable Recommendations in Creative Marketing · ACM Trans. Inf. Syst. 2025 SoCraft: Advertiser-level Predictive Scoring for Creative Performance on Meta · WSDM 2023 |
Recommender systems
click-through rate prediction |
0.9 | 1 | 2025 | Fusing Predictive and Large Language Models for Actionable Recommendations in Creative Marketing · ACM Trans. Inf. Syst. 2025 |
Machine learning › Generative modeling
generative adversarial network |
0.5 | 1 | 2021 | SoMin.ai: Personality-Driven Content Generation Platform · WSDM 2021 |
Recommender systems
user profiling |
0.5 | 1 | 2021 | SoMin.ai: Personality-Driven Content Generation Platform · WSDM 2021 |
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
large language model · 1.9multimodal deep neural network · 1.3mixture of experts · 1.3multi-view personality profiling · 1.0generative adversarial network · 1.0agentic AI · 1.0MBTI · 1.0multimodal models · 0.9explainability · 0.9behavioral science · 0.7user profiling · 0.3semantic search · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | SOMIN: Agentic AI for Automating Professional Visibility and Countering Content Homogenization
Aleksandr Farseev, Kirill Lepikhin, Kevin Manuel, Maksim Gorodilov, Zhao Kui, Ilya Makarov, Jaime Francisco Maldonado, Ian Cassidy, Ronan Byrne |
SIGIR | 2 |
| 2025 | Fusing Predictive and Large Language Models for Actionable Recommendations in Creative MarketingabstractThe opaqueness of modern digital advertising, exemplified by large platforms such as Meta Ads , raises concerns regarding their control over audience targeting, pricing structures, and ad relevancy assessments. Locked in place by network effects, these natural monopolies attract countless advertisers who rely on subjective intuition, with billions of dollars lost on ineffective social media advertisements. The platforms’ algorithms rely on huge amounts of data unavailable to advertisers, and the algorithms themselves are opaque too, so advertisers often cannot make informed decisions. To promote transparency and help individual advertisers, we first propose novel ways to optimize advertising strategies, predicting click-through rates of novel advertising content based on the content itself. However, advertisers face both opaqueness and a vast abundance of data: a large platform has so many competitor ads that it is hard to derive meaningful insights. Drawing inspiration from the success of Large Language Models (LLM), we propose a system that merges multimodal LLMs and pretrained AI models with an emphasis on digital marketing and advertising data analysis. Leveraging the capabilities of LLMs and incorporating explainability features, including modern text-image models, we aim to improve efficiency and produce synergy between human marketers and AI systems. Qi Yang 0005, Aleksandr Farseev, Marlo Ongpin, Alfred Huang, Yu-Yi Chu-Farseeva, Da-Min You, Kirill Lepikhin, Sergey I. Nikolenko |
ACM Trans. Inf. Syst. | 7 |
| 2023 | SoCraft: Advertiser-level Predictive Scoring for Creative Performance on MetaabstractIn this technical demonstration, we present SoCraft, a framework to build an advertiser-level multimedia ad content scoring platform for Meta Ads. The system utilizes a multimodal deep neural architecture to score and evaluate advertised content on Meta using both high- and low-level features of its contextual data such as text, image, targeting, and ad settings. In this demo, we present two deep models, SoDeep and SoWide, and validate the effectiveness of SoCraft with a successful real-world case study in Singapore. Alfred Huang, Qi Yang 0005, Sergey I. Nikolenko, Marlo Ongpin, Ilia Gossoudarev, Ngoc Yen Duong, Kirill Lepikhin, Sergey Vishnyakov, Yu-Yi Chu-Farseeva, Aleksandr Farseev |
WSDM | 7 |
| 2021 | SoMin.ai: Personality-Driven Content Generation PlatformabstractIn this technical demonstration, we showcase the World's first personality-driven marketing content generation platform, called SoMin.ai. The platform combines deep multi-view personality profiling framework and style generative adversarial networks facilitating the automatic creation of content that appeals to different human personality types. The platform can be used for enhancement of the social networking user experience as well as for content marketing routines. Guided by the MBTI personality type, automatically derived from a user social network content, SoMin.ai generates new social media content based on the preferences of other users with a similar personality type aiming at enhancing the user experience on social networking venues as well diversifying the efforts of marketers when crafting new content for digital marketing campaigns. The real-time user feedback to the platform via the platform's GUI fine-tunes the content generation model and the evaluation results demonstrate the promising performance of the proposed multi-view personality profiling framework when being applied in the content generation scenario. By leveraging content generation at a large scale, marketers will be able to execute more effective digital marketing campaigns at a lower cost. Aleksandr Farseev, Qi Yang 0005, Andrey Filchenkov, Kirill Lepikhin, Yu-Yi Chu-Farseeva, Daron-Benjamin Loo |
WSDM | 4 |
| 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 | 2 |