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

Authors:

Veronika Bogina,

Julia Sheidin,

Tsvi Kuflik,

Shlomo BerkovskyAuthors Info & Claims

AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces

Article No.: 69, Pages 1 - 5

https://doi.org/10.1145/3399715.3399813

Published: 02 October 2020 Publication History

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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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Freller LKeck MNeumayr TAugstein MBottoni PPanizzi E(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
https://dl.acm.org/doi/10.1145/3531073.3534484
Bogina VKuflik TJannach DBielikova MKompan MTrattner C(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
https://dl.acm.org/doi/10.1007/s11257-022-09335-w

Index Terms

Visualizing Program Genres' Temporal-Based Similarity in Linear TV Recommendations
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Information visualization

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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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Published: 02 October 2020

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

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AVI '20

AVI '20: International Conference on Advanced Visual Interfaces

September 28 - October 2, 2020

Salerno, Italy

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AVI '20 Paper Acceptance Rate 36 of 123 submissions, 29%;

Overall Acceptance Rate 128 of 490 submissions, 26%

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Freller LKeck MNeumayr TAugstein MBottoni PPanizzi E(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
https://dl.acm.org/doi/10.1145/3531073.3534484
Bogina VKuflik TJannach DBielikova MKompan MTrattner C(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
https://dl.acm.org/doi/10.1007/s11257-022-09335-w

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Recommendations

Sapling Similarity: A performing and interpretable memory-based tool for recommendation

Novel Item Recommendation by User Profile Partitioning

User Similarity Adjustment for Improved Recommendations

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