Visualizing program genres' temporal-based similarity in linear TV recommendations

Veronika Bogina, Julia Sheidin, Tsvi Kuflik, Shlomo Berkovsky

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the Working Conference on Advanced Visual Interfaces AVI 2020
EditorsGenny Tortora, Giuliana Vitiello, Marco Winckler
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages1-5
Number of pages5
ISBN (Electronic)9781450375351
DOIs
Publication statusPublished - 28 Sep 2020
Event2020 International Conference on Advanced Visual Interfaces, AVI 2020 - Salerno, Italy
Duration: 28 Sep 20202 Oct 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2020 International Conference on Advanced Visual Interfaces, AVI 2020
CountryItaly
CitySalerno
Period28/09/202/10/20

Keywords

  • recommender system
  • similarity
  • Visualization

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