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Quick Access: Building a Smart Experience for Google Drive

Published:13 August 2017Publication History

ABSTRACT

Google Drive is a cloud storage and collaboration service used by hundreds of millions of users around the world. Quick Access is a new feature in Google Drive that surfaces the most relevant documents when a user visits the home screen. Our metrics show that users locate their documents in half the time with this feature compared to previous approaches. The development of Quick Access illustrates many general challenges and constraints associated with practical machine learning such as protecting user privacy, working with data services that are not designed with machine learning in mind, and evolving product definitions. We believe that the lessons learned from this experience will be useful to practitioners tackling a wide range of applied machine learning problems.

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References

  1. Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI). 265--283.Google ScholarGoogle Scholar
  2. Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep Learning with Differential Privacy. In 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS). 308--318. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, and John Langford. 2014. A Reliable Effective Terascale Linear Learning System. Journal of Machine Learning Research 15, 1 (2014), 1111--1133.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Rakesh Agrawal and Ramakrishnan Srikant. 2000. Privacy-preserving Data Mining. In 2000 ACM SIGMOD International Conference on Management of Data (SIGMOD). 439--450. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Ashton Anderson, Ravi Kumar, Andrew Tomkins, and Sergei Vassilvitskii. 2014. The Dynamics of Repeat Consumption. In 23rd International World Wide Web Conference (WWW). 419--430. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Michael Bendersky, Xuanhui Wang, Donald Metzler, and Marc Najork. 2017. Learning from User Interactions in Personal Search via Attribute Parameterization. In 10th ACM International Conference on Web Search and Data Mining (WSDM). 791--799. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. James Bennett, Charles Elkan, Bing Liu, Padhraic Smyth, and Domonkos Tikk. 2007. KDD Cup and Workshop 2007. SIGKDD Explor. Newsl. 9, 2 (2007), 51--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Austin R. Benson, Ravi Kumar, and Andrew Tomkins. 2016. Modeling User Consumption Sequences. In 25th International Conference on World Wide Web (WWW). 519--529. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Craig Chambers, Ashish Raniwala, Frances Perry, Stephen Adams, Robert R. Henry, Robert Bradshaw, and Nathan Weizenbaum. 2010. FlumeJava: Easy, Efficient Data-parallel Pipelines. In 31st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). 363--375. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C Hsieh, Deborah A Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E Gruber. 2006. Bigtable: A distributed storage system for structured data. (2006), 205--2018.Google ScholarGoogle Scholar
  11. Jun Chen, Chaokun Wang, and Jianmin Wang. 2015. Will You "Reconsume" the Near Past? Fast Prediction on Short-term Reconsumption Behaviors. In 29th AAAI Conference on Artificial Intelligence (AAAI). 23--29.Google ScholarGoogle Scholar
  12. Michael Chui, James Manyika, Jacques Bughin, Richard Dobbs, Charles Roxburgh, Hugo Sarrazin, Geoffrey Sands, and Magdalena Westergren. 2012. The social economy: Unlocking value and productivity through social technologies. McKinsey Global Institute.Google ScholarGoogle Scholar
  13. Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In 10th ACM Conference on Recommender Systems (RecSys). 191--198. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. 2010. The YouTube Video Recommendation System. In 4th ACM Conference on Recommender Systems (RecSys). 293--296. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc-Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, and Andrew Y. Ng. 2012. Large Scale Distributed Deep Networks. In Advances in Neural Information Processing Systems 26 (NIPS). 1223--1231.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. John C. Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine Learning Research 12 (2011), 2121--2159.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Cynthia Dwork. 2006. Differential Privacy. In 33rd International Conference on Automata, Languages and Programming - Volume Part II (ICALP). 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdelrahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, and Brian Kingsbury. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29, 6 (2012), 82--97. Google ScholarGoogle ScholarCross RefCross Ref
  19. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 26 (NIPS). 1097--1105.Google ScholarGoogle Scholar
  20. Quoc V Le. 2013. Building high-level features using large scale unsupervised learning. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 8595--8598.Google ScholarGoogle ScholarCross RefCross Ref
  21. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521 (2015), 436--444. Google ScholarGoogle ScholarCross RefCross Ref
  22. Steffen Rendle, Dennis Fetterly, Eugene J. Shekita, and Bor-Yiing Su. 2016. Robust Large-Scale Machine Learning in the Cloud. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 1125--1134. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015. Hidden Technical Debt in Machine Learning Systems. In Advances in Neural Information Processing Systems 29 (NIPS). 2503--2511.Google ScholarGoogle Scholar
  24. Reza Shokri and Vitaly Shmatikov. 2015. Privacy-Preserving Deep Learning. In 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS). 1310--1321.Google ScholarGoogle Scholar
  25. Latanya Sweeney. 2002. K-anonymity: A Model for Protecting Privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10, 5 (2002), 557--570. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
      August 2017
      2240 pages
      ISBN:9781450348874
      DOI:10.1145/3097983

      Copyright © 2017 Owner/Author

      This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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      Association for Computing Machinery

      New York, NY, United States

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      • Published: 13 August 2017

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      KDD '17 Paper Acceptance Rate64of748submissions,9%Overall Acceptance Rate1,133of8,635submissions,13%

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