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 aim to learn representations for various entities from multiple entity interaction graphs that can be used in various downstream recommendation scenarios. The various entities involved evolve at different rates. Additionally, the downstream recommendation scenarios may be either jointly trained along with the representation or trained in a way that is decoupled from representation learning. The latter is likely to be the most common scenario. These different nuances are challenges that we need to overcome to build a practical system. In this talk, we present the work that has been done so far and the challenges we need to solve in the future.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
- Track:
- The Future of Search & Recommendation
- Date:
- Speakers:
- Arun Iyer
- Affiliation:
- Microsoft Research India
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Arun Iyer
Principal Researcher
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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: 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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Panel: Causality in search and recommendation systems
Speakers:- Emre Kiciman,
- Amit Sharma,
- Dean Eckles
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