The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Query type classification for web document retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
On the temporal dimension of search
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
ACM Transactions on Information Systems (TOIS)
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Answering general time sensitive queries
Proceedings of the 17th ACM conference on Information and knowledge management
Integration of news content into web results
Proceedings of the Second ACM International Conference on Web Search and Data Mining
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Improving search relevance for implicitly temporal queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Leveraging temporal dynamics of document content in relevance ranking
Proceedings of the third ACM international conference on Web search and data mining
Towards recency ranking in web search
Proceedings of the third ACM international conference on Web search and data mining
Ranking with query-dependent loss for web search
Proceedings of the third ACM international conference on Web search and data mining
Ranking specialization for web search: a divide-and-conquer approach by using topical RankSVM
Proceedings of the 19th international conference on World wide web
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
Adapting boosting for information retrieval measures
Information Retrieval
Freshness matters: in flowers, food, and web authority
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Understanding temporal query dynamics
Proceedings of the fourth ACM international conference on Web search and data mining
A language modeling approach for temporal information needs
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Mining anchor text trends for retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Multi-objective optimization in learning to rank
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Multi-objective ranking of comments on web
Proceedings of the 21st international conference on World Wide Web
Joint relevance and freshness learning from clickthroughs for news search
Proceedings of the 21st international conference on World Wide Web
Time-sensitive query auto-completion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Robust ranking models via risk-sensitive optimization
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Learning to rank search results for time-sensitive queries
Proceedings of the 21st ACM international conference on Information and knowledge management
Survival analysis for freshness in microblogging search
Proceedings of the 21st ACM international conference on Information and knowledge management
Real time discussion retrieval from twitter
Proceedings of the 22nd international conference on World Wide Web companion
Behavioral dynamics on the web: Learning, modeling, and prediction
ACM Transactions on Information Systems (TOIS)
The Impacts of Structural Difference and Temporality of Tweets on Retrieval Effectiveness
ACM Transactions on Information Systems (TOIS)
How fresh do you want your search results?
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Freshness of results is important in modern web search. Failing to recognize the temporal aspect of a query can negatively affect the user experience, and make the search engine appear stale. While freshness and relevance can be closely related for some topics (e.g., news queries), they are more independent in others (e.g., time insensitive queries). Therefore, optimizing one criterion does not necessarily improve the other, and can even do harm in some cases. We propose a machine-learning framework for simultaneously optimizing freshness and relevance, in which the trade-off is automatically adaptive to query temporal characteristics. We start by illustrating different temporal characteristics of queries, and the features that can be used for capturing these properties. We then introduce our supervised framework that leverages the temporal profile of queries (inferred from pseudo-feedback documents) along with the other ranking features to improve both freshness and relevance of search results. Our experiments on a large archival web corpus demonstrate the efficacy of our techniques.