Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Automatic web query classification using labeled and unlabeled training data
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Varying approaches to topical web query classification
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Ranking specialization for web search: a divide-and-conquer approach by using topical RankSVM
Proceedings of the 19th international conference on World wide web
Query-dependent rank aggregation with local models
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
| Hi-index | 0.00 |
In this paper, we proposed a novel divide-and-conquer approach to optimize the overall relevance in an unified framework for query clustering and query-based ranking. In our model, latent topics and specialized ranking models are learned iteratively so that an unified objective function, which lower-bounds the conditional probability of observed grades annotated by human editors on training data, is maximized. We conducted experiments comparing the proposed method with several baseline approaches on two data-sets. Experimental results illustrate that our method can significantly improve the ranking relevance over these baselines