An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data 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
Heads and tails: studies of web search with common and rare queries
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Similarity measures for short segments of text
ECIR'07 Proceedings of the 29th European conference on IR research
Optimal rare query suggestion with implicit user feedback
Proceedings of the 19th international conference on World wide web
Query similarity by projecting the query-flow graph
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Estimating advertisability of tail queries for sponsored search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Learning dense models of query similarity from user click logs
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Web search solved?: all result rankings the same?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning similarity function for rare queries
Proceedings of the fourth ACM international conference on Web search and data mining
LambdaMerge: merging the results of query reformulations
Proceedings of the fourth ACM international conference on Web search and data mining
Improving recommendation for long-tail queries via templates
Proceedings of the 20th international conference on World wide web
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It is well known that tail queries contribute to a substantial fraction of distinct queries submitted to search engines and thus become a major battle field for search engines. Unfortunately, compared with popular queries, it is much more difficult to obtain good search results for tail queries due to the lack of important relevance signals, such as user clicks, phrase matches and so on. In this paper, we propose to utilize the similarities between different queries to overcome the data sparsity problem for tail queries. Specifically, we propose to jointly learn query similarities and the ranking function from data so that the relevance signals of different but related queries can be collaboratively pooled to enhance the ranking of tail queries. We emphasize that the joint optimization is critical so that the learned query similarity function can adapt to the problem of learning ranking functions. Our proposed method is evaluated on two data sets and the results show that our method improves the relevance of tail queries over several baseline alternatives.