Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering
Ranking is an important task for handling a large amount of content. Ideally, training data for supervised ranking would include a complete rank of documents (or other objects such as images or videos) for a particular query. However, this is only possible for small sets of documents. In practice, o...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Omnipress
2011
|
| Online Access: | http://hdl.handle.net/20.500.11937/17402 |