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VideoReach: an online video recommendation system

Published: 23 July 2007 Publication History

Abstract

This paper presents a novel online video recommendation system called VideoReach, which alleviates users' efforts on finding the most relevant videos according to current viewings without a sufficient collection of user profiles as required in traditional recommenders. In this system, video recommendation is formulated as finding a list of relevant videos in terms of multimodal relevance (i.e. textual, visual, and aural relevance) and user click-through. Since different videos have different intra-weights of relevance within an individual modality and inter-weights among different modalities, we adopt relevance feedback to automatically find optimal weights by user click-though, as well as an attention fusion function to fuse multimodal relevance. We use 20 clips as the representative test videos, which are searched by top 10 queries from more than 13k online videos, and report superior performance compared with an existing video site.

References

[1]
Google Video. http://video.google.com/.
[2]
X.-S. Hua, L. Lu, and H.-J. Zhang. Optimization-based automated home video editing system. IEEE Trans. on CSVT, 14(5):572--583, May 2004.
[3]
X.-S. Hua and H.-J. Zhang. An attention-based decision fusion scheme for multimedia information retrieval. In Proceedings of PCM, 2004.
[4]
MovieFinder. http://www.moviefinder.com/.
[5]
MSN Soapbox. http://soapbox.msn.com/.
[6]
Y. Rui, T. S. Huang, and M. Ortega. Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. on CSVT, 8(5): 644--655, Sep 1998.
[7]
Yahoo! Video. http://video.yahoo.com/.
[8]
Y. Yang and X. Liu. A re-examination of text categorization methods. In Proceedings of ACM SIGIR, 1999.
[9]
Youtube. http://www.youtube.com/.

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  • (2024)Virtual Reality, Real Pedagogy: A Contextual Inquiry of Instructor Practices with VR VideoProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642510(1-21)Online publication date: 11-May-2024
  • (2023)An overview of video recommender systems: state-of-the-art and research issuesFrontiers in Big Data10.3389/fdata.2023.12816146Online publication date: 30-Oct-2023
  • (2023)SemVidRec: A Semantic Approach to Annotations Driven Video Recommendation Model Incorporating Machine IntelligenceComputational Intelligence and Network Systems10.1007/978-3-031-48984-6_1(1-13)Online publication date: 16-Dec-2023
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      cover image ACM Conferences
      SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
      July 2007
      946 pages
      ISBN:9781595935977
      DOI:10.1145/1277741
      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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      New York, NY, United States

      Publication History

      Published: 23 July 2007

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      Author Tags

      1. multimodal relevance
      2. video recommendation

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      SIGIR07
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      SIGIR07: The 30th Annual International SIGIR Conference
      July 23 - 27, 2007
      Amsterdam, The Netherlands

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      Overall Acceptance Rate 792 of 3,983 submissions, 20%

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      Cited By

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      • (2024)Virtual Reality, Real Pedagogy: A Contextual Inquiry of Instructor Practices with VR VideoProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642510(1-21)Online publication date: 11-May-2024
      • (2023)An overview of video recommender systems: state-of-the-art and research issuesFrontiers in Big Data10.3389/fdata.2023.12816146Online publication date: 30-Oct-2023
      • (2023)SemVidRec: A Semantic Approach to Annotations Driven Video Recommendation Model Incorporating Machine IntelligenceComputational Intelligence and Network Systems10.1007/978-3-031-48984-6_1(1-13)Online publication date: 16-Dec-2023
      • (2022)Preference-Tree-Based Real-Time Recommendation SystemEntropy10.3390/e2404050324:4(503)Online publication date: 2-Apr-2022
      • (2022)Multi-level feature learning approaches for video recommendation2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)10.1109/ASSIC55218.2022.10088325(1-6)Online publication date: 19-Nov-2022
      • (2020)Recommender Systems Leveraging Multimedia ContentACM Computing Surveys10.1145/340719053:5(1-38)Online publication date: 28-Sep-2020
      • (2019)Feature Re-Learning with Data Augmentation for Video Relevance PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2947442(1-1)Online publication date: 2019
      • (2019)An Efficient Personalized Video Recommendation Algorithm Based on Mixed Mode2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)10.1109/IUCC/DSCI/SmartCNS.2019.00088(367-373)Online publication date: Oct-2019
      • (2019)Building Effective Short Video Recommendation2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)10.1109/ICMEW.2019.00126(651-656)Online publication date: Jul-2019
      • (2019)MMM: Multi-source Multi-net Micro-video Recommendation with Clustered Hidden Item Representation LearningData Science and Engineering10.1007/s41019-019-00101-44:3(240-253)Online publication date: 6-Sep-2019
      • Show More Cited By

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