Search and information retrieval
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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…
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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…
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Research talk: Local factor models for large-scale inductive recommendation
In many domains, user preferences are similar locally within like-minded subgroups of users, but typically differ globally between those subgroups. Local recommendation models were shown to substantially improve top-k recommendation performance in such settings. However,…
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Research talk: Summarizing information across multiple documents and modalities
Search engines have evolved over time, from initially providing the most relevant URLs to user queries to providing information in response to user queries that summarize content from multiple web documents. Instead of clicking and…
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Research talk: Domain-specific pretraining for vertical search
Information overload is a prevalent challenge in many high-value domains. Search in biomedicine, and many other vertical domains, is challenging due to the scarcity of direct supervision from click logs. Self-supervised learning has emerged as…
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Research talk: IGLU: Interactive grounded language understanding in a collaborative environment
Human intelligence has the remarkable ability to quickly adapt to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new tasks by either imitating others’…
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Research talk: Challenges in multi-tenant graph representation learning for recommendation problems
Recent research has shown that representations learned from user-user and user-item graphs can be used to improve recommendation performance. In this research, the recommendation model is often trained with representation learning. In project DEEGO, we…
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Research talk: SPTAG++: Fast hundreds of billions-scale vector search with millisecond response time
Current state-of-the-art vector approximate nearest neighbor search (ANNS) libraries mainly focus on how to do fast high-recall search in memory. However, extremely large-scale vector search scenarios present certain challenges. For example, hundreds of billions of…
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Keynote: Universal search and recommendation
Search and recommendation are at the heart of how information workers deal with information overload. Recent advances and trends in science show us that search and recommendation will be transformed in the coming years. One…
Project
Table Interpretation
Bringing out the power of semantics in tabular data Tables are commonly used to organize information, playing a key role in data analytics, scientific research, and business communication. The ability to automatically extract semantics in…