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Interactive Recommender Systems: Tutorial

Published: 16 September 2015 Publication History

Abstract

In this tutorial we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In this tutorial, we outline the various aspects that are crucial for a smooth and effective user experience. In particular, we present our insights from several A/B tests. The tutorial will help researchers and practitioners in the RecSys community to gain a deeper understanding of the challenges related to the application of recommender systems in the online video and music entertainment business.

References

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S. Bostandjiev, J. O'Donovan, and T. Höllerer. Tasteweights: A visual interactive hybrid recommender system. In ACM Conference on Recommender Systems (RecSys), 2012.
[2]
N. Hariri, B. Mobasher, and R. Burke. Context Adaptation in Interactive Recommender Systems. In ACM Conference on Recommender Systems (RecSys), 2014.
[3]
E. Gansner, Y. Hu, S. Kobourov, and C. Volinsky. Putting recommendations on the map -- visualizing clusters and relations, 2009. AT&T Technical Report.
[4]
C. Johnson. Logistic Matrix Factorization for Implicit Feedback Data. In NIPS Workshop on Distributed Matrix Computations, 2014.
[5]
Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, pages 30--7, 2009.
[6]
B. N. Miller, I. Albert, S. K. Lam, J. A. Konstan, and J. Riedl. MovieLens unplugged: Experiences with an occasionally connected recommender system. In International Conference on Intelligent User Interfaces (IUI), 2003.
[7]
H. Steck. Item popularity and recommendation accuracy. In ACM Conference on Recommender Systems (RecSys), 2011.
[8]
S. Vargas, L. B., A. Karatzoglou, and P. Castells. Coverage, redundancy and size-awareness in genre diversity for recommender systems. In ACM Conference on Recommender Systems (RecSys), 2014.

Cited By

View all
  • (2024)What influences users to provide explicit feedback? A case of food delivery recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-023-09385-834:3(753-796)Online publication date: 1-Jul-2024
  • (2023)Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning ApproachACM Transactions on Information Systems10.1145/361810742:2(1-30)Online publication date: 8-Nov-2023
  • (2021)IRFProceedings of the ACM on Human-Computer Interaction10.1145/34492375:CSCW1(1-25)Online publication date: 22-Apr-2021
  • Show More Cited By

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  1. Interactive Recommender Systems: Tutorial

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    Published In

    cover image ACM Conferences
    RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems
    September 2015
    414 pages
    ISBN:9781450336925
    DOI:10.1145/2792838
    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: 16 September 2015

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    Author Tags

    1. interactive recommender systems
    2. personalization

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    • Tutorial

    Conference

    RecSys '15
    Sponsor:
    RecSys '15: Ninth ACM Conference on Recommender Systems
    September 16 - 20, 2015
    Vienna, Austria

    Acceptance Rates

    RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;
    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    Cited By

    View all
    • (2024)What influences users to provide explicit feedback? A case of food delivery recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-023-09385-834:3(753-796)Online publication date: 1-Jul-2024
    • (2023)Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning ApproachACM Transactions on Information Systems10.1145/361810742:2(1-30)Online publication date: 8-Nov-2023
    • (2021)IRFProceedings of the ACM on Human-Computer Interaction10.1145/34492375:CSCW1(1-25)Online publication date: 22-Apr-2021
    • (2020)Balancing Between Accuracy and Fairness for Interactive Recommendation with Reinforcement LearningAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47426-3_13(155-167)Online publication date: 6-May-2020
    • (2020)Content‐Based Health Recommender SystemsRecommender System with Machine Learning and Artificial Intelligence10.1002/9781119711582.ch11(215-236)Online publication date: 15-Jul-2020
    • (2018)Short-Term Satisfaction and Long-Term CoverageProceedings of the Eleventh ACM International Conference on Web Search and Data Mining10.1145/3159652.3159700(513-521)Online publication date: 2-Feb-2018
    • (2017)Code Recommendation with Natural Language Tags and Other Heterogeneous DataProceedings of the 2017 International Conference on Computer Science and Artificial Intelligence10.1145/3168390.3168407(137-142)Online publication date: 5-Dec-2017
    • (2017)Evaluating Different Strategies to Mitigate the Ramp-up Problem in Recommendation DomainsProceedings of the 23rd Brazillian Symposium on Multimedia and the Web10.1145/3126858.3126878(333-340)Online publication date: 17-Oct-2017
    • (2016)A Scrutable Algorithm for Enhancing the Efficiency of Recommender Systems using Fuzzy Decision TreeProceedings of the International Conference on Advances in Information Communication Technology & Computing10.1145/2979779.2979806(1-5)Online publication date: 12-Aug-2016

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