Search and information retrieval
Video
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,…
Video
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…
Video
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…
Video
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…
Video
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…
Video
Panel: Causality in search and recommendation systems
With the scale of search and recommendation, real-time robust and explainable decision-making is at the heart of search and recommendation systems that work robustly even as the user-base changes, new content appears, and topics rise…
Video
Research talk: Extracting pragmatics from content interactions to improve enterprise recommendations
Data trails, recording the way that people interact with content and with each other in an enterprise, are a source of linguistic pragmatics (cues to language meaning implied by social interactions) that can be used…
Video
Opening 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…
Video
Panel: The future of search and recommendation: Beyond web search
The increasing ability to learn representations of text, code, and even medical chemical compounds is changing what and how people search in every domain. In this panel, we bring together experts from industry and academia…
Video
Research talk: DeepXML: A deep extreme classification framework for recommending hundreds of millions of items
Extreme classification provides a formulation for large-scale ranking and recommendation problems by treating each item to be ranked or recommended as a separate label in a multi-label classification problem. Scalability and accuracy are well-recognized challenges…
