ABSTRACT
In this paper we examine the use of multi faceted recommendations to aid users while carrying out exploratory video retrieval tasks. These recommendations are integrated into ViGOR (Video Grouping, Organisation and Retrieval), a system which employs grouping techniques to facilitate video retrieval tasks. Two types of recommendations based on past usage history are utilised, the first attempts to couple the multi-faceted nature of explorative video retrieval tasks with the current user interests in order to provide global recommendations, while the second exploits the organisational features of ViGOR in order to provide recommendations based on a specific aspect of the user's task.
- Girgensohn, A., Shipman, F., Wilcox, L., Turner, T., and Cooper, M. MediaGLOW: organizing photos in a graph-based workspace. In Proc. IUI 2009, 419 -- 424. Google ScholarDigital Library
- Halvey, M., Vallet, D., Hannah, D., and Jose, J. M. ViGOR: a grouping oriented interface for search and retrieval in video libraries. In Proc. JCDL 2009, 87--96. Google ScholarDigital Library
- Hopfgartner, F., Vallet, D., Halvey, M., and Jose, J. Search trails using user feedback to improve video search. In Proc. ACM MM 2008, 339--348. Google ScholarDigital Library
- Urban, J. and Jose, J.M. A Personalised Multimedia Management and Retrieval Tool. In the International Journal of Intelligent Systems, 21(7), 725--745, 2006. Google ScholarDigital Library
Index Terms
- A multi faceted recommendation approach for explorative video retrieval tasks
Recommendations
Supporting exploratory video retrieval tasks with grouping and recommendation
Combine grouping of video search results with recommendation techniques to assist video retrieval.Evaluate grouping and recommendation techniques in separate evaluations to assess impact.Different recommendation approaches are relevant to the users at ...
Using a trust network to improve top-N recommendation
RecSys '09: Proceedings of the third ACM conference on Recommender systemsTop-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended ...
Typicality-Based Collaborative Filtering Recommendation
Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas ...
Comments