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 example is deep learning, which been revolutionizing search and recommendation through learned representations or embeddings. These trends raise the possibility of creating a universal search and recommendation system that provides push-button customized search and recommendation for any vertical, any data format, and any type of user need. This system would empower every business, regardless of size, to leverage the power of Microsoft’s AI within their search system and provide professionals across many disciplines the ability to improve their everyday workstreams. In this talk, we highlight trends in three areas that are essential to realizing this vision: representing knowledge with customizable universal embeddings, understanding users’ search needs across information communities and professions, and using hyper-personalization to create search systems beyond “”finding”” and help people discover.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
- 轨迹:
- The Future of Search & Recommendation
- 日期:
- 演讲者:
- Paul Bennett
- 所属机构:
- Microsoft Research Redmond
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Paul Bennett
Partner Research Manager
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The Future of Search & Recommendation
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Keynote: Universal search and recommendation
Speakers:- Paul Bennett
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Research talk: Learning and pretraining strategies for dense retrieval in search and beyond
Speakers:- Chenyan Xiong
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Research talk: Is phrase retrieval all we need?
Speakers:- Danqi Chen
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Research talk: IGLU: Interactive grounded language understanding in a collaborative environment
Speakers:- Julia Kiseleva
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Research talk: Summarizing information across multiple documents and modalities
Speakers:- Subhojit Som
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Panel: The future of search and recommendation: Beyond web search
Speakers:- Eric Horvitz,
- Nitin Agrawal,
- Soumen Chakrabati
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Research talk: Attentive knowledge-aware graph neural networks for recommendation
Speakers:- Yaming Yang
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Panel: Causality in search and recommendation systems
Speakers:- Emre Kiciman,
- Amit Sharma,
- Dean Eckles
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