ABSTRACT
With Internet delivery of video content surging to an un-precedented level, video recommendation has become a very popular online service. The capability of recommending relevant videos to targeted users can alleviate users' efforts on finding the most relevant content according to their current viewings or preferences. This paper presents a novel online video recommendation system based on multimodal fusion and relevance feedback. Given an online video document, which usually consists of video content and related information (such as query, title, tags, and surroundings), video recommendation is formulated as finding a list of the most relevant videos in terms of multimodal relevance. We express the multimodal relevance between two video documents as the combination of textual, visual, and aural relevance. Furthermore, since different video documents have different weights of the relevance for three modalities, we adopt relevance feedback to automatically adjust intra-weights within each modality and inter-weights among different modalities by users' click-though data, as well as attention fusion function to fuse multimodal relevance together. Unlike traditional recommenders in which a sufficient collection of users' profiles is assumed available, this proposed system is able to recommend videos without users' profiles. We conducted an extensive experiment on 20 videos searched by top 10 representative queries from more than 13k online videos, reported the effectiveness of our video recommendation system.
- http://soapbox.msn.com/.Google Scholar
- http://video.google.com/.Google Scholar
- http://video.msn.com/.Google Scholar
- http://video.yahoo.com/.Google Scholar
- http://www.myspace.com/.Google Scholar
- http://www.youtube.com/.Google Scholar
- R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 1999. Google ScholarDigital Library
- M. Balabanovic. Exploring versus exploiting when learning user models for text recommendation. User Modeling and User-Adapted Interaction, 8(4):71--102, Nov 1998. Google ScholarDigital Library
- C. Christakou and A. Stafylopatis. A hybrid movie recommender system based on neural networks. In Proceedings of the 2005 5th International Conference on Intelligent Systems Design and Applications, Wroclaw, Poland, 2005. Google ScholarDigital Library
- A. G. Hauptmann, W. H. Lin, R. Yan, J. Yang, and M. Y. Chen. Extreme video retrieval: Joint maximization of human and computer performance. In Proceedings of the ACM International Conference on Multimedia, Santa Barbara, USA, 2006. Google ScholarDigital Library
- W. H. Hsu, L. S. Kennedy, and S.-F. Chang. Video search reranking via information bottleneck principle. In Proceedings of the ACM International Conference on Multimedia, Santa Barbara, USA, 2006. Google ScholarDigital Library
- X.-S. Hua, L. Lu, and H.-J. Zhang. Optimization-based automated home video editing system. IEEE Trans. on Circuit and System for Video Technology, 14(5):572--583, May 2004. Google ScholarDigital Library
- X.-S. Hua, T. Mei, W. Lai, and et al. Microsoft Research Asia TRECVID 2006 high-level feature extraction and rushes exploitation. In TREC Video Retrieval Evaluation Online Proceedings, 2006.Google Scholar
- X.-S. Hua and H.-J. Zhang. An attention-based decision fusion scheme for multimedia information retrieval. In Proceedings of IEEE Pacific-Rim Conference On Multimedia, Tokyo, Japan, 2004. Google ScholarDigital Library
- M. S. Lew, N. Sebe, C. Djeraba, and R. Jain. Content-based multimedia information retrieval: State of the art and challenges. ACM Trans. on Multimedia Computing, Communications and Applications, 2(1):1--19, Feb 2006. Google ScholarDigital Library
- H. Mak, I. Koprinska, and J. Poon. INTIMATE: A web-based movie recommender using text categorization. In Proceedings of the IEEE/WIC International Conference on Web Intelligence, Beijing, China, 2003. Google ScholarDigital Library
- Online Publishers. http://www.online-publishers.org/.Google Scholar
- P. Resnick and H. R. Varian. Recommender systems. Communications of the ACM, 40(3):56--58, May 1997. Google ScholarDigital Library
- Y. Rui, T. S. Huang, and M. Ortega. Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. on Circuit and System for Video Technology, 8(5):644--655, Sep 1998. Google ScholarDigital Library
- M. V. Setten and M. Veenstra. Prediction strategies in a TV recommender system - method and experiments. In Proceedings of International World Wide Web Conference, Budapest, Hungary, 2003.Google Scholar
- Y. Yang and X. Liu. A re-examination of text categorization methods. In Proceedings of ACM SIGIR conference on Research and development in information retrieval, California, USA, 1999. Google ScholarDigital Library
Index Terms
- Online video recommendation based on multimodal fusion and relevance feedback
Recommendations
Contextual Video Recommendation by Multimodal Relevance and User Feedback
With Internet delivery of video content surging to an unprecedented level, video recommendation, which suggests relevant videos to targeted users according to their historical and current viewings or preferences, has become one of most pervasive online ...
An online video recommendation framework using rich information
ICIMCS '11: Proceedings of the Third International Conference on Internet Multimedia Computing and ServiceAutomatic video recommendation is involved in an attempt to tackle the information-overload problem, aiming to present the personalized video list to the user. This paper presents a novel approach to improve the accuracy of the video recommendation by ...
Multimodal retrieval with relevance feedback based on genetic programming
This paper presents a framework for multimodal retrieval with relevance feedback based on genetic programming. In this supervised learning-to-rank framework, genetic programming is used for the discovery of effective combination functions of (multimodal)...
Comments