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ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering

Authors:

Denis Kotkov,

Qian Zhao,

Kati Launis,

Mats NeoviusAuthors Info & Claims

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

Pages 432 - 437

https://doi.org/10.1145/3383313.3412221

Published: 22 September 2020 Publication History

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Abstract

Related item recommendations have a long history in recommender systems, but they tend to be a static list of similar items with respect to a target item of interest without any support of user control. In this paper, we propose ClusterExplorer, a novel approach for enabling user control over related recommendations. The approach allows users to explore the latent space of user-item interactions through controlling related recommendations. We evaluated ClusterExplorer in the book domain with 42 participants recruited in a public library and found that our approach has higher user satisfaction of browsing items and is more helpful in finding interesting items compared to traditional related item recommendations.

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Cited By

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Kotkov DMedlar AKask TGlowacka D(2024)The Dark Matter of Serendipity in Recommender SystemsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638342(108-118)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638342
Kotkov D(2024)Overview of Serendipity in Recommender SystemsWeb Engineering10.1007/978-3-031-62362-2_43(453-457)Online publication date: 16-Jun-2024
https://doi.org/10.1007/978-3-031-62362-2_43
Kavitha VKoteeswaran S(2023)Study on Book Recommendation System2023 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)10.1109/ACCTHPA57160.2023.10083372(1-8)Online publication date: 20-Jan-2023
https://doi.org/10.1109/ACCTHPA57160.2023.10083372
Show More Cited By

ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and Clustering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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Information & Contributors

Information

Published In

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

September 2020

796 pages

ISBN:9781450375832

DOI:10.1145/3383313

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

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 September 2020

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Short-paper
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Refereed limited

Funding Sources

Academy of Finland

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RecSys '20

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RecSys '20: Fourteenth ACM Conference on Recommender Systems

September 22 - 26, 2020

Virtual Event, Brazil

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

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Cited By

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Kotkov DMedlar AKask TGlowacka D(2024)The Dark Matter of Serendipity in Recommender SystemsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638342(108-118)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638342
Kotkov D(2024)Overview of Serendipity in Recommender SystemsWeb Engineering10.1007/978-3-031-62362-2_43(453-457)Online publication date: 16-Jun-2024
https://doi.org/10.1007/978-3-031-62362-2_43
Kavitha VKoteeswaran S(2023)Study on Book Recommendation System2023 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)10.1109/ACCTHPA57160.2023.10083372(1-8)Online publication date: 20-Jan-2023
https://doi.org/10.1109/ACCTHPA57160.2023.10083372
Sun RAkella AKong RZhou MKonstan J(2023)Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field ExperimentInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226279640:22(7233-7247)Online publication date: 5-Oct-2023
https://doi.org/10.1080/10447318.2023.2262796
Kotkov DMaslov ANeovius MDiaz FShah CSuel TCastells PJones RSakai T(2021)Revisiting the Tag Relevance Prediction ProblemProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463019(1768-1772)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463019

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