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Interactive evaluation of recommender systems with SNIPER: an episode mining approach

Authors:

Sandy Moens,

Olivier Jeunen,

Bart GoethalsAuthors Info & Claims

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Pages 538 - 539

https://doi.org/10.1145/3298689.3346965

Published: 10 September 2019 Publication History

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Abstract

Recommender systems are typically evaluated using either offline methods, online methods, or through user studies. In this paper we take an episode mining approach to analysing recommender system data and we demonstrate how we can use SNIPER, a tool for interactive pattern mining, to analyse and understand the behaviour of recommender systems. We describe the required data format, and present a useful scenario of how a user can interact with the system to answer questions about the quality of recommendations.

References

[1]

Boris Cule, Nikolaj Tatti, and Bart Goethals. 2014. Marbles: Mining association rules buried in long event sequences. Statistical Analysis and Data Mining: The ASA Data Science Journal 7, 2 (2014), 93--110.

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Google Scholar

[2]

Bart Goethals. 2003. Survey on frequent pattern mining. Univ. of Helsinki 19 (2003), 840--852.

Google Scholar

[3]

Bart Goethals, Sandy Moens, and Jilles Vreeken. 2011. MIME: a framework for interactive visual pattern mining. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 757--760.

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Google Scholar

[4]

Asela Gunawardana and Guy Shani. 2015. Evaluating recommender systems. In Recommender systems handbook. Springer, 265--308.

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Cited By

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Ouarem ONouioua FFournier‐Viger P(2023)A survey of episode miningWIREs Data Mining and Knowledge Discovery10.1002/widm.152414:2Online publication date: 28-Nov-2023
https://doi.org/10.1002/widm.1524
Tseng JHsieh STseng V(2022)A Scalable Analytical Framework for Complex Event Episode Mining With Various Domains ApplicationsIEEE Access10.1109/ACCESS.2022.322896210(130672-130685)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3228962
De Pauw J(2020)Exploratory Methods for Evaluating Recommender SystemsProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3411456(782-786)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1145/3383313.3411456
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Index Terms

Interactive evaluation of recommender systems with SNIPER: an episode mining approach
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

September 2019

635 pages

ISBN:9781450362436

DOI:10.1145/3298689

General Chairs:
Toine Bogers
Aalborg University Copenhagen, Denmark
,
Alan Said
University of Gothenburg, Sweden
,
Program Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Domonkos Tikk
Gravity R&D, Hungary

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: 10 September 2019

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RecSys '19

RecSys '19: Thirteenth ACM Conference on Recommender Systems

September 16 - 20, 2019

Copenhagen, Denmark

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RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;

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

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Cited By

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Ouarem ONouioua FFournier‐Viger P(2023)A survey of episode miningWIREs Data Mining and Knowledge Discovery10.1002/widm.152414:2Online publication date: 28-Nov-2023
https://doi.org/10.1002/widm.1524
Tseng JHsieh STseng V(2022)A Scalable Analytical Framework for Complex Event Episode Mining With Various Domains ApplicationsIEEE Access10.1109/ACCESS.2022.322896210(130672-130685)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3228962
De Pauw J(2020)Exploratory Methods for Evaluating Recommender SystemsProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3411456(782-786)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1145/3383313.3411456
Feremans LVercruyssen VMeert WCule BGoethals B(2019)A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event LogsNew Frontiers in Mining Complex Patterns10.1007/978-3-030-48861-1_1(3-20)Online publication date: 16-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-48861-1_1

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Cited By

Index Terms

Recommendations

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Temporal diversity in recommender systems

Acquiring User Information Needs for Recommender Systems