Fab: content-based, collaborative recommendation
Communications of the ACM
IEEE Transactions on Knowledge and Data Engineering
Interval Data Clustering with Applications
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
CollaboraTV: making television viewing social again
Proceedings of the 1st international conference on Designing interactive user experiences for TV and video
Towards Better Content Visibility in Video Recommender Systems
FCST '08 Proceedings of the 2008 Japan-China Joint Workshop on Frontier of Computer Science and Technology
A Scalable, Accurate Hybrid Recommender System
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
Recommender Systems Handbook
Recommender Systems: An Introduction
Recommender Systems: An Introduction
Multicriteria User Modeling in Recommender Systems
IEEE Intelligent Systems
A social network-based recommender system
A social network-based recommender system
Personality-based recommender systems: an overview
Proceedings of the sixth ACM conference on Recommender systems
Hi-index | 0.00 |
The amount of video content that is available on the web grows at each instant. This fact implicates in an important issue -- video content overload. One way to treat such problem consists on the use of recommender systems. In this sense, this paper proposes a method to enhance the accuracy of the predictions given by video recommender systems by the use of Segments of Interest (SOI). Based on the premise that users tend to like particular segments of a video more than the entire video, and that they are able to mark these segments, these can be used to identify similar people, i.e. the ones who have similar interests about videos. This similarity can be used to enhance the accuracy of the ratings predictions of traditional collaborative video recommender systems. To evaluate this approach, an experimental evaluation was performed. The results showed that the accuracy improvement is directly related with the level of participation of people marking SOI. Thus, as more people collaborate and interact, better will be the recommendation result.