Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A multi-ranker model for adaptive XML searching
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
| Hi-index | 0.00 |
In order to deal with the diversified nature of XML documents as well as individual user preferences, we propose a novel Multi-Ranker Model (MRM), which is able to abstract a spectrum of important XML properties and adapt the features to different XML search needs. The model consists of a novel three-level ranking structure and a training module called Ranking Support Vector Machine in a voting Spy Na¨1ve Bayes Framework (RSSF). RSSF is effective in learning search preference and then ranks the returned results adaptively. In this demonstration, we present our prototype developed from the model, which we call it the MRM XML search engine. The MRM engine employs only a list of simple XML tagged keywords as a user query for searching XML fragments from a collection of real XML documents. The demonstration presents an indepth analyses of the effectiveness of adaptive rankers, tailored XML rankers and a spectrum of low level ranking features.