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Searching for Communities: a Facebook Way

Published: 18 July 2019 Publication History

Abstract

Giving people the power to build community is central to Facebook's mission. Technically, searching for communities poses very different challenges compared to the standard IR problems. First, there is a vocabulary mismatch problem since most of the content of the communities is private. Second, the common labeling strategies based on human ratings and clicks do not work well due to limited public content available to third-party raters and users at search time. Finally, community search has a dual objective of satisfying searchers and growing the number of active communities. While A/B testing is a well known approach for assessing the former, it is an open question on how to measure progress on the latter. This talk discusses these challenges in depth and describes our solution.

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MP4 File (cite1-17h00-d2.mp4)

References

[1]
Viet Ha-Thuc and Shakti Sinha. 2016. Learning to Rank Personalized Search Results in Professional Networks. In Proceedings of the 39th ACM SIGIR. 461--462.
[2]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. CoRR, Vol. abs/1301.3781 (2013).

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cover image ACM Conferences
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2019
1512 pages
ISBN:9781450361729
DOI:10.1145/3331184
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New York, NY, United States

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Published: 18 July 2019

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Author Tags

  1. causal effect
  2. counterfactual
  3. embeddings
  4. explainability
  5. privacy

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SIGIR '19
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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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