IEEE Transactions on Knowledge and Data Engineering
MultiTube--Where Web 2.0 and Multimedia Could Meet
IEEE MultiMedia
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
VideoReach: an online video recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Online Index Recommendations for High-Dimensional Databases Using Query Workloads
IEEE Transactions on Knowledge and Data Engineering
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Hybrid web recommender systems
The adaptive web
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Dynamic captioning: video accessibility enhancement for hearing impairment
Proceedings of the international conference on Multimedia
Enhancing accessibility of microblogging messages using semantic knowledge
Proceedings of the 20th ACM international conference on Information and knowledge management
IEEE Transactions on Multimedia
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Personalized video recommendation through tripartite graph propagation
Proceedings of the 20th ACM international conference on Multimedia
Mining user similarity based on routine activities
Information Sciences: an International Journal
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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.