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A large scale machine learning system for recommending heterogeneous content in social networks

Published: 24 July 2011 Publication History

Abstract

The goal of the Facebook recommendation engine is to compare and rank heterogeneous types of content in order to find the most relevant recommendations based on user preference and page context. The challenges for such a recommendation engine include several aspects: 1) the online queries being processed are at very large scale; 2) with new content types and new user-generated content constantly added to the system, the candidate object set and underlying data distribution change rapidly; 3) different types of content usually have very distinct characteristics, which makes generic feature engineering difficult; and 4) unlike a search engine that can capture intention of users based on their search queries, our recommendation engine needs to focus more on users' profile and interests, past behaviors and current actions in order to infer their cognitive states. In this presentation, we would like to introduce an effective, scalable, online machine learning framework we developed in order to address the aforementioned challenges. We also want to discuss the insights, approaches and experiences we have accumulated during our research and development process.

Cited By

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  • (2022)Towards Practical Personalized Security Nudge Schemes: Investigating the Moderation Effects of Behavioral Features on Nudge EffectsScience of Cyber Security10.1007/978-3-031-17551-0_33(505-521)Online publication date: 30-Sep-2022
  • (2014)Question recommendation with constraints for massive open online coursesProceedings of the 8th ACM Conference on Recommender systems10.1145/2645710.2645748(49-56)Online publication date: 6-Oct-2014
  • (2013)Patent partner recommendation in enterprise social networksProceedings of the sixth ACM international conference on Web search and data mining10.1145/2433396.2433404(43-52)Online publication date: 4-Feb-2013
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      cover image ACM Conferences
      SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
      July 2011
      1374 pages
      ISBN:9781450307574
      DOI:10.1145/2009916

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 24 July 2011

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      Author Tags

      1. ctr prediction
      2. large scale system
      3. online learning

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      Overall Acceptance Rate 792 of 3,983 submissions, 20%

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      • (2022)Towards Practical Personalized Security Nudge Schemes: Investigating the Moderation Effects of Behavioral Features on Nudge EffectsScience of Cyber Security10.1007/978-3-031-17551-0_33(505-521)Online publication date: 30-Sep-2022
      • (2014)Question recommendation with constraints for massive open online coursesProceedings of the 8th ACM Conference on Recommender systems10.1145/2645710.2645748(49-56)Online publication date: 6-Oct-2014
      • (2013)Patent partner recommendation in enterprise social networksProceedings of the sixth ACM international conference on Web search and data mining10.1145/2433396.2433404(43-52)Online publication date: 4-Feb-2013
      • (2012)Cross-domain collaboration recommendationProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2339530.2339730(1285-1293)Online publication date: 12-Aug-2012

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