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Visualizing Program Genres' Temporal-Based Similarity in Linear TV Recommendations

Published: 02 October 2020 Publication History

Abstract

There is an increasing evidence that data visualization is an important and useful tool for quick understanding and filtering of large amounts of data. In this paper, we contribute to this body of work with a study that compares chord and ranked list for presentation of a temporal TV program genre similarity in next-program recommendations. We consider genre similarity based on the similarity of temporal viewing patterns. We discover that chord presentation allows users to see the whole picture and improves their ability to choose items beyond the ranked list of top similar items. We believe that similarity visualization may be useful for the provision of both the recommendations and their explanations to the end users.

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Cited By

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  • (2022)Towards a Construction Kit for Visual Recommender SystemsProceedings of the 2022 International Conference on Advanced Visual Interfaces10.1145/3531073.3534484(1-3)Online publication date: 6-Jun-2022
  • (2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022

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  1. Visualizing Program Genres' Temporal-Based Similarity in Linear TV Recommendations

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    cover image ACM Other conferences
    AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces
    September 2020
    613 pages
    ISBN:9781450375351
    DOI:10.1145/3399715
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 02 October 2020

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    Author Tags

    1. Visualization
    2. recommender system
    3. similarity

    Qualifiers

    • Short-paper
    • Research
    • Refereed limited

    Funding Sources

    • Israeli Science Foundation (ISF)
    • CyCAT -European Union's Horizon 2020 Research and Innovation Program

    Conference

    AVI '20
    AVI '20: International Conference on Advanced Visual Interfaces
    September 28 - October 2, 2020
    Salerno, Italy

    Acceptance Rates

    AVI '20 Paper Acceptance Rate 36 of 123 submissions, 29%;
    Overall Acceptance Rate 128 of 490 submissions, 26%

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    Cited By

    View all
    • (2022)Towards a Construction Kit for Visual Recommender SystemsProceedings of the 2022 International Conference on Advanced Visual Interfaces10.1145/3531073.3534484(1-3)Online publication date: 6-Jun-2022
    • (2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022

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