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Or David

dblp:364/1017 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2026
0009-0003-0491-9604ORCID · reported

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

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Databases, 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.

Theoretical computer science
1 paper
Distributed computing theory · 67% Logic in computer science · 33%

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

TopicWeightPapersLastEvidence papers
Logic in computer science › epistemic logic
common knowledge
1.012026
Brief Announcement: What is Agreement About if not Common Knowledge? · PODC 2026
Distributed computing theory
consensus
1.012026
Brief Announcement: What is Agreement About if not Common Knowledge? · PODC 2026
Distributed computing theory › distributed algorithms
distributed coordination
1.012026
Brief Announcement: What is Agreement About if not Common Knowledge? · PODC 2026

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

epistemic logic · 1.0
YearPublicationVenuePosition
2026 Brief Announcement: What is Agreement About if not Common Knowledge?
Or David, Yoram Moses
PODC1
2023 Audience Prospecting for Dynamic-Product-Ads in Native Advertising
abstract
With yearly revenue exceeding one billion USD, Yahoo Gemini native advertising marketplace serves more than two billion impressions daily to hundreds of millions of unique users. One of the fastest growing segments of Gemini native is dynamic-product-ads (DPA), where major advertisers, such as Amazon and Walmart, provide catalogs with millions of products for the system to choose from and present to users. The subject of this work is finding and expanding the right audience for each DPA ad, which is one of the many challenges DPA presents. Approaches such as targeting various user groups, e.g., users who already visited the advertisers’ websites (Retargeting), users that searched for certain products (Search-Prospecting), or users that reside in preferred locations (Location-Prospecting), have limited audience expansion capabilities.In this work we present two new approaches for audience expansion that also maintain predefined performance goals. The Conversion-Prospecting approach predicts DPA conversion rates based on Gemini native logged data, and calculates the expected cost-per-action (CPA) for determining users’ eligibility to products and optimizing DPA bids in Gemini native auctions. To support new advertisers and products, the Trending-Prospecting approach matches trending products to users by learning their tendency towards products from advertisers’ sites logged events. The tendency scores indicate the popularity of the product and the similarity of the user to those who have previously engaged with this product.The two new prospecting approaches were tested online, serving real Gemini native traffic, demonstrating impressive DPA delivery and DPA revenue lifts while maintaining most traffic within the acceptable CPA range (i.e., performance goal). In particular, we measure 10% delivery lift and 1.58% revenue lift for Conversion-Prospecting, and almost 13% delivery lift and 6.33% revenue lift for Trending-Prospecting. After a successful testing phase, the proposed approaches are currently in production and serve all Gemini native traffic.
Eliran Abutbul, Yohay Kaplan, Naama Krasne, Oren Somekh, Or David, Omer Duvdevany, Evgeny Segal
IEEE Big Data5