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User Group based Viewpoint Recommendation using User Attributes for Multiview Videos

Published: 27 October 2017 Publication History

Abstract

Multiview videos can provide diverse information and high flexibility in enhancing the viewing experience. User-dependent automatic viewpoint recommendation is important for reducing user stress while selecting continually suitable and favorable viewpoints. Existing personal viewpoint recommendation methods have been developed by learning the user»s viewing records. These methods have difficulty in acquiring sufficient personal viewing records in practice. Moreover, they neglect the importance of the user»s attribute information, such as user personality, interest, and experience level in the viewing content. Thus, we propose a group-based recommendation framework consisting of a user grouping approach based on the similarity in existing user viewing records, and a member group estimation approach based on the classification by user attributes. We validate the effectiveness of the proposed group-based recommendation and analyze the relationship between user attributes and multiview viewing patterns.

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cover image ACM Conferences
MUSA2 '17: Proceedings of the Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes
October 2017
78 pages
ISBN:9781450355094
DOI:10.1145/3132515
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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Publication History

Published: 27 October 2017

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Author Tags

  1. big five personality trait
  2. multi-view video
  3. user clustering
  4. viewpoint recommendation

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MM '17
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MM '17: ACM Multimedia Conference
October 27, 2017
California, Mountain View, USA

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Cited By

View all
  • (2025)MANSY: Generalizing Neural Adaptive Immersive Video Streaming With Ensemble and Representation LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.348717524:3(1654-1668)Online publication date: Mar-2025
  • (2022)Personalized 360-Degree Video StreamingProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548047(3143-3151)Online publication date: 10-Oct-2022
  • (2022)Prediction Algorithm of Hashtags for Image Posting Social Network ServicesThe Review of Socionetwork Strategies10.1007/s12626-022-00126-816:2(291-305)Online publication date: 17-Sep-2022
  • (2021)Construction of a Switching Support System for Live Broadcast of Oral PresentationJournal of Information Processing10.2197/ipsjjip.29.20629(206-214)Online publication date: 2021
  • (2019)User-Aware Folk Popularity RankProceedings of the 27th ACM International Conference on Multimedia10.1145/3343031.3350920(1970-1978)Online publication date: 15-Oct-2019

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