research-article

WTF: the who to follow service at Twitter

Authors:

Reza ZadehAuthors Info & Claims

WWW '13: Proceedings of the 22nd international conference on World Wide Web

Pages 505 - 514

https://doi.org/10.1145/2488388.2488433

Published: 13 May 2013 Publication History

Abstract

WTF ("Who to Follow") is Twitter's user recommendation service, which is responsible for creating millions of connections daily between users based on shared interests, common connections, and other related factors. This paper provides an architectural overview and shares lessons we learned in building and running the service over the past few years. Particularly noteworthy was our design decision to process the entire Twitter graph in memory on a single server, which significantly reduced architectural complexity and allowed us to develop and deploy the service in only a few months. At the core of our architecture is Cassovary, an open-source in-memory graph processing engine we built from scratch for WTF. Besides powering Twitter's user recommendations, Cassovary is also used for search, discovery, promoted products, and other services as well. We describe and evaluate a few graph recommendation algorithms implemented in Cassovary, including a novel approach based on a combination of random walks and SALSA. Looking into the future, we revisit the design of our architecture and comment on its limitations, which are presently being addressed in a second-generation system under development.

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Cited By

Fang PLi ZKhan ALuo SWang FShi ZFeng D(2025)Information-Oriented Random Walks and Pipeline Optimization for Distributed Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342433337:1(408-422)Online publication date: Jan-2025
https://doi.org/10.1109/TKDE.2024.3424333
Wang HBaeza-Yates RBonchi F(2024)Revisiting Local PageRank Estimation on Undirected Graphs: Simple and OptimalProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671820(3036-3044)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671820
Zhu ZWang KLiu HLi JLuo SBaeza-Yates RBonchi F(2024)Topology-monitorable Contrastive Learning on Dynamic GraphsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671777(4700-4711)Online publication date: 25-Aug-2024
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Index Terms

WTF: the who to follow service at Twitter
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Other conferences

WWW '13: Proceedings of the 22nd international conference on World Wide Web

May 2013

1628 pages

ISBN:9781450320351

DOI:10.1145/2488388

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 13 May 2013

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WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Paper Acceptance Rate 125 of 831 submissions, 15%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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https://doi.org/10.1109/TKDE.2024.3424333
Wang HBaeza-Yates RBonchi F(2024)Revisiting Local PageRank Estimation on Undirected Graphs: Simple and OptimalProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671820(3036-3044)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671820
Zhu ZWang KLiu HLi JLuo SBaeza-Yates RBonchi F(2024)Topology-monitorable Contrastive Learning on Dynamic GraphsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671777(4700-4711)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671777
Zhu ZLuo SLin WWang SMo DLi C(2024)Personalized PageRanks over Dynamic Graphs - The Case for Optimizing Quality of Service2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00038(409-422)Online publication date: 13-May-2024
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Angles RFerrada SBurgos I(2024)The Property Graph Data Format (PGDF)IEEE Access10.1109/ACCESS.2024.348568512(159267-159279)Online publication date: 2024
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Ortega SMartin-Delgado M(2024)Randomized SearchRank: A semiclassical approach to a quantum search enginePhysical Review Research10.1103/PhysRevResearch.6.0430146:4Online publication date: 4-Oct-2024
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Muñoz PDíez FBellogín A(2024)Modeling disinformation networks on Twitter: structure, behavior, and impactApplied Network Science10.1007/s41109-024-00610-w9:1Online publication date: 26-Jan-2024
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Loukil MSfaxi LRobbana R(2024)How to create graphs in hardware-constrained environments? Choosing the best creation approach via machine learning-based predictive modelsInternational Journal of Data Science and Analytics10.1007/s41060-023-00495-5Online publication date: 2-Feb-2024
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Dutta LMaji GSen S(2024)Mutual Interest-Based Twitter Followee Recommendation Using Latent Dirichlet Allocation Topic ModellingJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-01125-9Online publication date: 9-Aug-2024
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Li JShomer HMao HZeng SMa YShah NTang JYin DOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Evaluating graph neural networks for link predictionProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666291(3853-3866)Online publication date: 10-Dec-2023
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