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You are what apps you use: Transfer Learning for Personalized Content and Ad Recommendation

Published: 27 August 2017 Publication History

Abstract

Cold start is always a key challenge for building real-life recommendation systems. Thanks to the ever-growing multi-modal data in the mobile Internet age and the latest deep learning techniques, transfer-learning based cross-domain recommendation starts to play a crucial role in tackling the cold start problem and to provide "warm-start" recommendation for new users. At Cheetah Mobile, we apply transfer learning to build personalized recommendation systems for both advertisement and content scenarios, serving 600+ millions monthly active mobile users. In particular, we leveraged the app install & usage and many other mobile data, built a Unified User Profile (UUP) by using transfer learning and deep learning, and developed cross-domain personalized Ad and news recommendation. Our approaches enable us to solve the cold start problem with close to full coverage of our user base while yielding significant CTR increase and better user experience.

References

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Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching Word Vectors with Subword Information. CoRR (2016).
[2]
Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191--198.
[3]
Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015. A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems. In WWW.
[4]
Heng-Tze Cheng et al. 2016. Wide & Deep Learning for Recommender Systems. In DLRS.
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Jie Tang, Sen Wu, Jimeng Sun, and Hang Su. 2012. Cross-domain Collaboration Recommendation. In KDD.

Cited By

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  • (2021)Understanding the Long-term Dynamics of Mobile App Usage Context via Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3110141(1-1)Online publication date: 2021
  • (2020)Personalized Context-aware Collaborative Online Activity PredictionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33698293:4(1-28)Online publication date: 14-Sep-2020
  • (2020)Masked-field Pre-training for User Intent PredictionProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412726(2789-2796)Online publication date: 19-Oct-2020

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cover image ACM Conferences
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems
August 2017
466 pages
ISBN:9781450346528
DOI:10.1145/3109859
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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Author Tags

  1. personalized recommendation
  2. transfer learning
  3. user profile

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RecSys '17
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RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2021)Understanding the Long-term Dynamics of Mobile App Usage Context via Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3110141(1-1)Online publication date: 2021
  • (2020)Personalized Context-aware Collaborative Online Activity PredictionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33698293:4(1-28)Online publication date: 14-Sep-2020
  • (2020)Masked-field Pre-training for User Intent PredictionProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412726(2789-2796)Online publication date: 19-Oct-2020

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