M
Mihajlo Grbovic
Researcher at Airbnb
Publications - 71
Citations - 2462
Mihajlo Grbovic is an academic researcher from Airbnb. The author has contributed to research in topics: Web search query & Personalization. The author has an hindex of 23, co-authored 71 publications receiving 2067 citations. Previous affiliations of Mihajlo Grbovic include Temple University & Yahoo!.
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Proceedings ArticleDOI
Hate Speech Detection with Comment Embeddings
Nemanja Djuric,Jing Zhou,Robin D. Morris,Mihajlo Grbovic,Vladan Radosavljevic,Narayan Bhamidipati +5 more
TL;DR: This work proposes to learn distributed low-dimensional representations of comments using recently proposed neural language models, that can then be fed as inputs to a classification algorithm, resulting in highly efficient and effective hate speech detectors.
Proceedings ArticleDOI
Real-time Personalization using Embeddings for Search Ranking at Airbnb
Mihajlo Grbovic,Haibin Cheng +1 more
TL;DR: The embedding models were specifically tailored for Airbnb marketplace, and are able to capture guest's short-term and long-term interests, delivering effective home listing recommendations.
Proceedings ArticleDOI
E-commerce in Your Inbox: Product Recommendations at Scale
Mihajlo Grbovic,Vladan Radosavljevic,Nemanja Djuric,Narayan Bhamidipati,Jaikit Savla,Varun Bhagwan,Doug Sharp +6 more
TL;DR: In this article, a system that leverages user purchase history determined from e-mail receipts to deliver highly personalized product ads to Yahoo Mail users is described, which was evaluated against baselines that included showing popular products and products predicted based on co-occurrence.
Proceedings ArticleDOI
Context- and Content-aware Embeddings for Query Rewriting in Sponsored Search
TL;DR: This work proposes rewriting method based on a novel query embedding algorithm, which jointly models query content as well as its context within a search session, and shows the proposed approach significantly outperformed existing state-of-the-art, strongly indicating its benefits and the monetization potential.
Proceedings ArticleDOI
Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content
TL;DR: Extensions to the proposed model, which can be applied to personalized recommendation and social relationship mining by adding further user layers to the hierarchy, thus learning user-specific vectors to represent individual preferences, are discussed.