Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Using temporal profiles of queries for precision prediction
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Solving large scale linear prediction problems using stochastic gradient descent algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Temporal document retrieval model for business news archives
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th 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
Improving search relevance for implicitly temporal queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Clustering and exploring search results using timeline constructions
Proceedings of the 18th ACM conference on Information and knowledge management
Leveraging temporal dynamics of document content in relevance ranking
Proceedings of the third ACM international conference on Web search and data mining
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
Use of temporal expressions in web search
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
TAER: time-aware entity retrieval-exploiting the past to find relevant entities in news articles
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Determining time of queries for re-ranking search results
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
Understanding temporal query dynamics
Proceedings of the fourth ACM international conference on Web search and data mining
Learning to rank for freshness and relevance
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Ranking related news predictions
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Detecting seasonal queries by time-series analysis
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Temporal ranking of search engine results
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
A language modeling approach for temporal information needs
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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Retrieval effectiveness of temporal queries can be improved by taking into account the time dimension. Existing temporal ranking models follow one of two main approaches: 1) a mixture model linearly combining textual similarity and temporal similarity, and 2) a probabilistic model generating a query from the textual and temporal part of document independently. In this paper, we propose a novel time-aware ranking model based on learning-to-rank techniques. We employ two classes of features for learning a ranking model, entity-based and temporal features, which are derived from annotation data. Entity-based features are aimed at capturing the semantic similarity between a query and a document, whereas temporal features measure the temporal similarity. Through extensive experiments we show that our ranking model significantly improves the retrieval effectiveness over existing time-aware ranking models.