research-article

The effect of different video summarization models on the quality of video recommendation based on low-level visual features

Authors:

Yashar Deldjoo,

Paolo Cremonesi,

Massimo QuadranaAuthors Info & Claims

CBMI '17: Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing

Article No.: 20, Pages 1 - 6

https://doi.org/10.1145/3095713.3095734

Published: 19 June 2017 Publication History

Abstract

Video summarization is a powerful tool for video understanding and browsing and is considered as an enabler for many video analysis tasks. While the effect of video summarization models has been largely studied in video retrieval and indexing applications over the last decade, its impact has not been well investigated in content-based video recommendation systems (RSs) based on low-level visual features, where the goal is to recommend items/videos to users based on visual content of videos. This work reveals specific problems related to video summarization and their impact on video recommendation. We present preliminary results of an analysis involving applying different video summarization models for the problem of video recommendation on a real-world RS dataset (MovieLens-10M) and show how temporal feature aggregation and video segmentation granularity can significantly influence/improve the quality of recommendation.

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Cited By

Basavarajaiah MSharma P(2019)Survey of Compressed Domain Video Summarization TechniquesACM Computing Surveys10.1145/335539852:6(1-29)Online publication date: 16-Oct-2019
https://dl.acm.org/doi/10.1145/3355398
Zhang HHe ZLi J(2019)Accelerating the Deep Reinforcement Learning with Neural Network Compression2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852451(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852451
Zhang JGao MYu JWang XSong YXiong Q(2019)Nonlinear Transformation for Multiple Auxiliary Information in Music Recommendation2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851992(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8851992
Show More Cited By

Index Terms

The effect of different video summarization models on the quality of video recommendation based on low-level visual features
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Other conferences

CBMI '17: Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing

June 2017

237 pages

ISBN:9781450353335

DOI:10.1145/3095713

Copyright © 2017 ACM.

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: 19 June 2017

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CBMI '17

CBMI '17: International Workshop on Content-Based Multimedia Indexing

June 19 - 21, 2017

Florence, Italy

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Cited By

Basavarajaiah MSharma P(2019)Survey of Compressed Domain Video Summarization TechniquesACM Computing Surveys10.1145/335539852:6(1-29)Online publication date: 16-Oct-2019
https://dl.acm.org/doi/10.1145/3355398
Zhang HHe ZLi J(2019)Accelerating the Deep Reinforcement Learning with Neural Network Compression2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852451(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852451
Zhang JGao MYu JWang XSong YXiong Q(2019)Nonlinear Transformation for Multiple Auxiliary Information in Music Recommendation2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851992(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8851992
Zhang YWang ZDu J(2019)Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851942(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8851942
Zhang QRao LYang Y(2019)DGFFM: Generalized Field-aware Factorization Machine based on DenseNet2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851933(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8851933
Deldjoo YDacrema MConstantin MEghbal-Zadeh HCereda SSchedl MIonescu BCremonesi P(2019)Movie genomeUser Modeling and User-Adapted Interaction10.1007/s11257-019-09221-y29:2(291-343)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11257-019-09221-y
Deldjoo Y(2019)Enhancing Video Recommendation Using Multimedia ContentSpecial Topics in Information Technology10.1007/978-3-030-32094-2_6(77-89)Online publication date: 1-Oct-2019
https://doi.org/10.1007/978-3-030-32094-2_6
Aggarwal SGoswami DHooda MChakravarty AKar AVasudha (2019)Recommendation Systems for Interactive Multimedia EntertainmentData Visualization and Knowledge Engineering10.1007/978-3-030-25797-2_2(23-48)Online publication date: 10-Aug-2019
https://doi.org/10.1007/978-3-030-25797-2_2
Deldjoo YSchedl MHidasi BKnees PPera SEkstrand MAmatriain XO'Donovan J(2018)Multimedia recommender systemsProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3241620(537-538)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3241620
Deldjoo YConstantin MEghbal-Zadeh HIonescu BSchedl MCremonesi PPera SEkstrand MAmatriain XO'Donovan J(2018)Audio-visual encoding of multimedia content for enhancing movie recommendationsProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240407(455-459)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240407
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