RecSysIL Community

Recommender Systems Unleashed

Scaling Personalization and Creativity Across Industries

  • Sunday, January 5, 2025
  • 18:00-20:00
  • Taboola Offices*
Join our meetup!

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Guy Feigenblat

Yevgeny Tkach

Asi Messica

Discover how recommender systems shape industries like e-commerce, news, and creativity!

Hear from experts at eBay, Taboola, and Lightricks as they share insights on scaling personalization, tackling unique data challenges, and unlocking creative potential through innovative algorithms.

Join us for an evening of tech insights and networking!

What to Expect?

Recommending at scale - personalizing the eBay shopping journey

Explore how eBay enhances shopping with AI and Big Data for a global audience.
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Picture of Guy Feigenblat

Guy Feigenblat

Senior Manager, Buyer Experience

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Building Content Recommendation Systems for News Sites with Sparse Data at Scale

Discover Taboola’s approach to scalable, high-precision recommendation systems.
Read More ➜
Picture of Yevgeny Tkach

Yevgeny Tkach

Machine Learning Team Leader

View Bio ➜

When Recommender Systems Meet Creativity

Find out how Lightricks leverages multimodal AI to match creators and enhance editing tools.
Read More ➜
Picture of Asi Messica

Asi Messica

VP Data Science

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Agenda

  • 18:00-18:30   Gathering
  • 18:30-19:00   Recommending at scale - personalizing the eBay shopping journey | Guy Feigenblat
  • 19:00-19:30   Building Content Recommendation Systems for News Sites with Sparse Data at Scale | Yevgeny Tkach
  • 19:30-20:00   When Recommender Systems Meet Creativity | Asi Messica
  • Sunday, January 5, 2025
  • 18:00-20:00

*Taboola offices, Atrium Tower, Zeev Jabotinsky St 2, Ramat Gan, 23rd floor

Join our meetup!

Recommending at scale - personalizing the eBay shopping journey

eBay is a global online marketplace that connects millions of buyers and sellers, facilitating the exchange of goods across a vast range of categories. With over 2.1 billion items sold annually and 130 million active buyers across 190 marketplaces worldwide, eBay faces unique challenges in delivering personalized experiences at scale.
In this talk, we will explore the advanced recommender systems developed by the Buyer Experience AI group at eBay, focusing on how we leverage Big Data to optimize the shopping experience. We will discuss the challenges of working with massive datasets, the methods used to extract user interests, and the techniques for personalizing recommendations to enhance the shopping journey.
Additionally, we will delve into how we identify and support “good shopping missions,” ensuring users find exactly what they need in an efficient and engaging manner.
Picture of Guy Feigenblat

Guy Feigenblat

Senior Manager, Buyer Experience

View Bio ➜

Guy Feigenblat

Senior Manager, Buyer Experience

Senior Manager at eBay R&D, where he leads the IL Buyer Experience AI group within the eBay Marketplace. In his current role, Guy oversees the applied research and engineering of recommender systems on eBay’s Home Page, tackling complex challenges in AI, machine learning, and Big Data to improve the platform’s user experience.
Before joining eBay, Guy served as AI Director at Piiano, where he spearheaded research in privacy discovery and engineering, focusing on advancing privacy-preserving technologies.
Earlier in his career, Guy was a Team Leader and Research Staff Member at IBM Research AI, where he focused on Natural Language Processing (NLP), Information Retrieval (IR), and AI, with a particular emphasis on document summarization.

He has authored numerous patents and published many papers in leading venues. Guy earned his Ph.D. in Computer Science from Bar-Ilan University.

Building Content Recommendation Systems for News Sites with Sparse Data at Scale

Delivering personalized content on large-scale platforms, such as news sites, presents unique challenges due to the vast number of users, items, and the sparsity of interaction data.
This talk provides an in-depth look at the architecture of Taboola’s recommendation systems, focusing on the two-stage process of candidation and reranking. We’ll explore how deep learning architectures are leveraged to tackle these challenges, achieving a balance between scalability and precision.
Additionally, we’ll share insights and discuss the challenges faced when developing high-performance recommendation systems at scale.
Picture of Yevgeny Tkach

Yevgeny Tkach

Machine Learning Team Leader

View Bio ➜

Yevgeny Tkach

Machine Learning Team Leader

Machine learning team leader focusing on real time bidding algorithms to serve recommendations.
Data scientist in the ad tech industry for the last 10 years.

When Recommender Systems Meet Creativity

Lightricks’ photo and video editing tools unlock endless possibilities for self-expression, while its creator services empower content creators to monetize their talents and collaborate with leading brands.
In this talk, we’ll provide an overview of the diverse recommender systems powering Lightricks’ products and take a deep dive into the algorithms and architecture of our multimodal recommender system. We will explore two distinct use cases, each with its own unique constraints and challenges: matching brands with creators who align with their values and campaign goals, and recommending the most suitable features for a specific image a user has uploaded.
Picture of Asi Messica

Asi Messica

VP Data Science

View Bio ➜

Asi Messica

VP Data Science

Dr. Asi Messica is VP Data Science at Lightricks and a lecturer at Reichman University. Before joining Lightricks, she held various data science, product, and development management positions at Fiverr, SAP, RSA, and more.
She pursued her Ph.D. in the area of Recommender Systems at Ben-Gurion University. Her professional interests include machine learning, personalization, information retrieval, NLP and reinforcement learning.