GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
User Modeling and User-Adapted Interaction
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
VISCORS: A Visual-Content Recommender for the Mobile Web
IEEE Intelligent Systems
IEEE Transactions on Image Processing
Unsupervised feature and model selection for generalized Dirichlet mixture models
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
| Hi-index | 0.00 |
This paper presents a generative graphical model (VC-Aspect) for filtering visual documents such as images. The proposed VC-Aspect extends the well-known Aspect model and combines both content based and collaborative filtering approaches in a unified framework. Instead of considering item indices in the model such as model-based collaborative filtering techniques, we use visual features in describing visual documents. This allows the model to predict ratings for new visual documents with the same set of parameters. Experimental results show the usefulness of such an approach in a real life application such as the content based image retrieval.