2009 Proceeding- Conference Chairs:
- Nick Craswell,
- Rosie Jones,
- Georges Dupret,
- Evelyne Viegas
No abstract available.
Proceeding Downloads
Survey and evaluation of query intent detection methods
User interactions with search engines reveal three main underlying intents, namely navigational, informational, and transactional. By providing more accurate results depending on such query intents the performance of search engines can be greatly ...
Analysis of long queries in a large scale search log
We propose to use the search log to study long queries, in order to understand the types of information needs that are behind them, and to design techniques to improve search effectiveness when they are used. Long queries arise in many different ...
Distinguishing humans from robots in web search logs: preliminary results using query rates and intervals
The workload on web search engines is actually multiclass, being derived from the activities of both human users and automated robots. It is important to distinguish between these two classes in order to reliably characterize human web search behavior, ...
Incremental learning to rank with partially-labeled data
In this paper we present a semi-supervised learning method for a problem of learning to rank where we exploit Markov random walks and graph regularization in order to incorporate not only "labeled" web pages but also plenty of "un-labeled" web pages (...
Generating unambiguous URL clusters from web search
This paper reports on the generation of unambiguous clusters of URLs from clickthrough data from the MSN search query log excerpt (the RFP 2006 dataset). Selections (clickthroughs) by a single user from a single query can be assumed to have some mutual ...
Topic-specific analysis of search queries
The analysis of search engine logs is important in order to understand how users interact with a search engine. Conventional analysis of search engine log data looks at various metrics such as query and session length aggregated over the full data set. ...
Optimising topical query decomposition
Topical query decomposition (TQD) is a paradigm recently introduced in [1], which, given a query, returns to the user a set of queries that cover the answer set of the original query. The TQD problem was studied as a variant of the set-cover problem and ...
Search shortcuts using click-through data
Major Web Search Engines take as a common practice to provide Suggestions to users in order to enhance their search experience. Such suggestions have normally the form of queries that are, to some extent, "similar" to the queries already submitted by ...
Query suggestions using query-flow graphs
The query-flow graph [Boldi et al., CIKM 2008] is an aggregated representation of the latent querying behavior contained in a query log. Intuitively, in the query-flow graph a directed edge from query qi to query qj means that the two queries are likely ...
Using query logs and click data to create improved document descriptions
Logfiles of search engines are a promising resource for data mining, since they provide raw data associated to users and web documents. In this paper we focus on the latter aspect and explore how the information in logfiles could be used to improve ...
Intentional query suggestion: making user goals more explicit during search
The degree to which users' make their search intent explicit can be assumed to represent an upper bound on the level of service that search engines can provide. In a departure from traditional query expansion mechanisms, we introduce Intentional Query ...
Usefulness of quality click-through data for training
Modern Information Retrieval (IR) systems often employ document weighting models with many parameters that require to be appropriately set for effective retrieval performance. To obtain these parameter settings, quality training is usually required, ...
Comparative analysis of clicks and judgments for IR evaluation
Queries and click-through data taken from search engine transaction logs is an attractive alternative to traditional test collections, due to its volume and the direct relation to end-user querying. The overall aim of this paper is to answer the ...
Tailoring click models to user goals
Click models provide a principled way of understanding user interaction with web search results in a query session and a statistical tool for leveraging search engine click logs to analyze and improve user experience. An important component in all ...
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- Macdonald C, Tonellotto N and Ounis I Efficient & Effective Selective Query Rewriting with Efficiency Predictions Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, (495-504)
- Cai F, Liang S and de Rijke M (2016). Prefix-Adaptive and Time-Sensitive Personalized Query Auto Completion, IEEE Transactions on Knowledge and Data Engineering, 28:9, (2452-2466), Online publication date: 1-Sep-2016.
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Cai F, Reinanda R and Rijke M (2016). Diversifying Query Auto-Completion, ACM Transactions on Information Systems, 10.1145/2910579, 34:4, (1-33), Online publication date: 9-Jun-2016.
- Shuang Z and Wang N Classification Model for Query Logs Based on Intent Mining Proceedings of the 2015 Seventh International Conference on Advanced Communication and Networking (ACN), (68-71)
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Wang C and Chen H (2013). Intent mining in search query logs for automatic search script generation, Knowledge and Information Systems, 10.1007/s10115-013-0620-3, 39:3, (513-542), Online publication date: 1-Jun-2014.
- Macdonald C, Santos R and Ounis I (2013). The whens and hows of learning to rank for web search, Information Retrieval, 16:5, (584-628), Online publication date: 1-Oct-2013.
- Wang C, Lin Y, Tsai M and Chen H (2013). Mining subtopics from different aspects for diversifying search results, Information Retrieval, 16:4, (452-483), Online publication date: 1-Aug-2013.
- Tonellotto N, Macdonald C and Ounis I Efficient and effective retrieval using selective pruning Proceedings of the sixth ACM international conference on Web search and data mining, (63-72)
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Wang C, Huang H and Chen H (2012). Automatic construction of an evaluation dataset from wisdom of the crowds for information retrieval applications 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC, 10.1109/ICSMC.2012.6377772, 978-1-4673-1714-6, (490-495)
- He J, Hollink V and de Vries A Combining implicit and explicit topic representations for result diversification Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, (851-860)
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Lee L and Chen H (2011). Mining search intents for collaborative cyberporn filtering, Journal of the American Society for Information Science and Technology, 10.1002/asi.21668, 63:2, (366-376), Online publication date: 1-Feb-2012.
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Gil-Costa V, Santos R, Macdonald C and Ounis I (2011). Sparse Spatial Selection for Novelty-Based Search Result Diversification String Processing and Information Retrieval, 10.1007/978-3-642-24583-1_34, (344-355),
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Macdonald C, Tonellotto N and Ounis I (2011). On Upper Bounds for Dynamic Pruning Advances in Information Retrieval Theory, 10.1007/978-3-642-23318-0_29, (313-317),
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Sushmita S, Piwowarski B and Lalmas M (2010). Dynamics of Genre and Domain Intents Information Retrieval Technology, 10.1007/978-3-642-17187-1_39, (399-409),
- Pitler E and Church K Using word-sense disambiguation methods to classify web queries by intent Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3, (1428-1436)