Abstract
Video recommendation is a hot research topic to help people access interesting videos. The existing video recommendation approaches include CBF, CF and HF. However, these approaches treat the relationships between all users as equal and neglect an important fact that the acquaintances or friends may be a more reliable source than strangers to recommend interesting videos. Thus, in this paper we propose a novel approach to improve the accuracy of video recommendation. For a given user, our approach calculates a recommendation score for each video candidate that composes of two parts: the interest degree of this video by the user’s friends, and the relationship strengths between the user and his friends. The final recommended videos are ranked according to the accumulated recommendation scores from different recommenders. We conducted experiments with 45 participants and the results demonstrated the feasibility and effectiveness of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 734–749 (2005)
Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D., Aly, M.: Video suggestion and discovery for youtube: taking random walks through the view graph. In: ACM WWW, pp. 895–904 (2008)
Boll, S.: Multitube–where web 2.0 and multimedia could meet. IEEE Multimedia 14(1), 9–13 (2007)
Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)
Cilibrasi, R., Vitanyi, P.: The google similarity distance. IEEE Transactions on Knowledge and Data Engineering, 370–383 (2007)
Gibas, M., Canahuate, G., Ferhatosmanoglu, H.: Online index recommendations for high-dimensional databases using query workloads. IEEE Transactions on Knowledge and Data Engineering, 246–260 (2008)
Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: ACM CHI, pp. 211–220 (2009)
Hong, R., Wang, M., Xu, M., Yan, S., Chua, T.: Dynamic captioning: video accessibility enhancement for hearing impairment. In: ACM MM, pp. 421–430 (2010)
Hu, X., Tang, L., Liu, H.: Enhancing accessibility of microblogging messages using semantic knowledge. In: ACM CIKM (2011)
Mei, T., Aizawa, K.: Video recommendation. In: Chapter of Internet Multimedia Search and Mining. Bentham Science Publisher (2011)
Mei, T., Yang, B., Hua, X., Yang, L., Yang, S., Li, S.: Videoreach: an online video recommendation system. In: ACM SIGIR, pp. 767–768 (2007)
Park, J., Lee, S., Kim, K., Chung, B., Lee, Y.: An online video recommendation framework using view based tag cloud aggregation. IEEE Multimedia (99), 1 (2010)
Wang, M., Hua, X., Tang, J., Hong, R.: Beyond distance measurement: constructing neighborhood similarity for video annotation. IEEE Transactions on Multimedia 11(3), 465–476 (2009)
Wang, M., Hua, X., Tang, J., Qi, G., Song, Y.: Unified video annotation via multi-graph learning. IEEE Transactions on Circuits and Systems for Video Technology 19(5) (2009)
Wang, M., Yang, K., Hua, X.-S., Zhang, H.-J.: Towards a relevant and diverse search of social images. IEEE Transactions on Multimedia, 12 (2010)
Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: ACM WWW, pp. 981–990 (2010)
Yang, Y., Xu, D., Nie, F., Yan, S., Zhuang, Y.: Image clustering using local discriminant models and global integration. IEEE Transactions on Image Processing (2010)
Yang, Y., Zhuang, Y., Wu, F., Pan, Y.: Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval. IEEE Transactions on Multimedia, 10 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, X., Yuan, J., Hong, R., Wang, M., Li, Z., Chua, TS. (2012). On Video Recommendation over Social Network. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-27355-1_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27354-4
Online ISBN: 978-3-642-27355-1
eBook Packages: Computer ScienceComputer Science (R0)