tutorial

Tutorial: Lessons Learned from Building Real-life Recommender Systems

Authors:

Xavier Amatriain,

Deepak AgarwalAuthors Info & Claims

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Page 433

https://doi.org/10.1145/2959100.2959194

Published: 07 September 2016 Publication History

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Abstract

In 2006, Netflix announced a \$1M prize competition to advance recommendation algorithms. The recommendation problem was simplified as the accuracy in predicting a user rating measured by the Root Mean Squared Error. While that formulation helped get the attention of the research community, it put the focus on the wrong approach and metric while leaving many important factors out. In this tutorial we will describe the advances in Recommender Systems in the last 10 years from an industry perspective based on the instructors' personal experience at companies like Quora, LinkedIn, Netflix, or Yahoo! We will do so in the form of different lessons learned through the years.

Some of those lessons will describe the different components of modern recommender systems such as: personalized ranking, similarity, explanations, context-awareness, or multi-armed bandits. Others will also review the usage of novel algorithmic approaches such as Factorization Machines, Restricted Boltzmann Machines, SimRank, Deep Neural Networks, or Listwise Learning-to-rank. Others will dive into details of the importance of gathering the right data or using the correct optimization metric.

But, most importantly, we will give many examples of prototypical industrial-scale recommender systems with special focus on those unsolved challenges that should define the future of the recommender systems area.

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References

[1]

Recommender Systems in Industry: A Netflix Case Study. X Amatriain, J Basilico, Recommender Systems Handbook, 385--419

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Personalizing LinkedIn Feed. Deepak Agarwal et al. KDD 2015

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Data Mining Methods for Recommender Systems. X Amatriain, JM Pujol, Recommender Systems Handbook, 227--262

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Mining large streams of user data for personalized recommendations. X Amatriain, ACM SIGKDD Explorations Newsletter 14 (2), 37--48

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Recommending items to users: an explore/exploit perspective. Deepak Agarwal UEO@CIKM 2013: 1--2

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Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. A Karatzoglou, X Amatriain, L Baltrunas, N Oliver. Proceedings of the fourth ACM conference on Recommender systems, 79--86

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I like it... i like it not: Evaluating user ratings noise in recommender systems. X Amatriain, JM Pujol, N Oliver. User modeling, adaptation, and personalization, 247--258

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Toslali MParthasarathy SOliveira FHuang HCoskun A(2021)Iter8Proceedings of the ACM Symposium on Cloud Computing10.1145/3472883.3486984(289-304)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1145/3472883.3486984
Goldenberg DKofman KAlbert JMizrachi SHorowitz ATeinemaa ILewin-Eytan LCarmel DYom-Tov EAgichtein EGabrilovich E(2021)Personalization in Practice: Methods and ApplicationsProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441657(1123-1126)Online publication date: 8-Mar-2021
https://dl.acm.org/doi/10.1145/3437963.3441657
Frigó EKocsis L(2021)Online convex combination of ranking modelsUser Modeling and User-Adapted Interaction10.1007/s11257-021-09306-732:4(649-683)Online publication date: 6-Nov-2021
https://doi.org/10.1007/s11257-021-09306-7
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Index Terms

Tutorial: Lessons Learned from Building Real-life Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Information & Contributors

Information

Published In

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

September 2016

490 pages

ISBN:9781450340359

DOI:10.1145/2959100

General Chairs:
Shilad Sen
Macalester College, USA
,
Werner Geyer
IBM Research Cambridge, USA
,
Program Chairs:
Jill Freyne
CSIRO, Australia
,
Pablo Castells
Universidad Autónoma de Madrid, Spain

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

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Published: 07 September 2016

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RecSys '16

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SIGCHI

RecSys '16: Tenth ACM Conference on Recommender Systems

September 15 - 19, 2016

Massachusetts, Boston, USA

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RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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Toslali MParthasarathy SOliveira FHuang HCoskun A(2021)Iter8Proceedings of the ACM Symposium on Cloud Computing10.1145/3472883.3486984(289-304)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1145/3472883.3486984
Goldenberg DKofman KAlbert JMizrachi SHorowitz ATeinemaa ILewin-Eytan LCarmel DYom-Tov EAgichtein EGabrilovich E(2021)Personalization in Practice: Methods and ApplicationsProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441657(1123-1126)Online publication date: 8-Mar-2021
https://dl.acm.org/doi/10.1145/3437963.3441657
Frigó EKocsis L(2021)Online convex combination of ranking modelsUser Modeling and User-Adapted Interaction10.1007/s11257-021-09306-732:4(649-683)Online publication date: 6-Nov-2021
https://doi.org/10.1007/s11257-021-09306-7
Gorgoglione MPanniello UTuzhilin A(2019)RECOMMENDATION STRATEGIES IN PERSONALIZATION APPLICATIONSInformation & Management10.1016/j.im.2019.01.005Online publication date: Jan-2019
https://doi.org/10.1016/j.im.2019.01.005
Protasov SKhan ASozykin KAhmad M(2018)Using deep features for video scene detection and annotationSignal, Image and Video Processing10.1007/s11760-018-1244-612:5(991-999)Online publication date: 24-Jan-2018
https://doi.org/10.1007/s11760-018-1244-6
Ning YShi YHong LRangwala HRamakrishnan NCremonesi PRicci FBerkovsky STuzhilin A(2017)A Gradient-based Adaptive Learning Framework for Efficient Personal RecommendationProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109909(23-31)Online publication date: 27-Aug-2017
https://dl.acm.org/doi/10.1145/3109859.3109909

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