A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Computational Statistics & Data Analysis - Nonlinear methods and data mining
The influence of caption features on clickthrough patterns in web search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning web query patterns for imitating Wikipedia articles
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhanced results for web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Improving search result summaries by using searcher behavior data
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Eye tracking experiments have shown that titles of Web search results play a crucial role in guiding a user's search process. We present a machine-learned algorithm that trains a boosted tree to pick the most relevant title for a Web search result. We compare two modeling approaches: i) using absolute editorial judgments and ii) using pairwise preference judgments. We find that the pairwise modeling approach gives better results in terms of three offline metrics. We present results of our models in four regions. We also describe a hybrid user satisfaction evaluation process -- search success -- that combines page relevance and user click behavior, and show that our machine-learned algorithm improves in search success.