Search and information retrieval
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Research talk: The science behind semantic search: How AI from Microsoft Bing is powering Azure Cognitive Search
Azure Cognitive Search is a cloud search service that gives developers APIs and tools to build rich search experiences over private, heterogeneous content in web, mobile, and enterprise applications. As part of our AI at…
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Research talk: Learning and pretraining strategies for dense retrieval in search and beyond
In this talk, we’ll quickly go through our recent observations and findings with dense retrieval. First, we’ll recap the standard setup and training strategies for dense retrieval models in search. We’ll then share our recent…
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Keynote: Extreme classification for dense retrieval and personalized recommendation
Extreme classification is a new research area pioneered by scientists at Microsoft dealing with classification problems involving millions, or even billions, of categories. In this keynote, partner researcher Manik Varma demonstrates how extreme classification can…
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Research talk: Approximate nearest neighbor search systems at scale
Building deep learning-based search and recommendation systems at internet scale requires a complete redesign of the search index. Key to this redesign is a fast, accurate, and cost-efficient indexing system for approximate nearest neighbor search.…
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Research talk: Capturing the visual evolution of fashion in space and time
The fashion domain is a magnet for computer vision. New vision problems are emerging in step with the fashion industry’s rapid evolution towards an online, social, and personalized business. Style models, trend forecasting, and recommendation…
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Research talk: System frontiers for dense retrieval
The Microsoft Bing search engine combines classic information retrieval and dense retrieval in multiple stages of the search funnel. Handling hundreds of billions of documents with constant updates creates massive system challenges to inference, search,…
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Research talk: Search, summarization, and sensemaking
Natural language processing (NLP) has undergone head-spinning advances over the last 5–10 years. At the same time, user interfaces for search have remained somewhat static. Has NLP advanced enough to more actively aid searchers in the sensemaking…
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Research talk: Attentive knowledge-aware graph neural networks for recommendation
To alleviate data sparsity and cold-start problems of traditional recommender systems (RSs), incorporating knowledge graphs (KGs) to supplement auxiliary information has attracted considerable attention recently. Since the construction of these KGs is independent of the…
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Research talk: Is phrase retrieval all we need?
DensePhrases is an extractive phrase-search tool based on natural language input that achieves dense retrieval of billion-scale phrases with extreme runtime efficiency. In this talk, Assistant Professor Danqi Chen of Princeton University will highlight some…
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Closing remarks: The Future of Search and Recommendation
Search and recommendation is core to many Microsoft offerings—such as Microsoft 365, Microsoft Azure, and Microsoft Bing—and it’s crucial to a growing range of industries, such as biomedicine, retail e-commerce, and legal. Underlying technologies are transforming what we know about search and recommendation and the ability to…
