Publication statistics

Pub. period:2004-2012
Pub. count:21
Number of co-authors:15



Co-authors

Number of publications with 3 favourite co-authors:

Lillian Lee:6
David Carmel:3
Carmel Domshlak:3

 

 

Productive colleagues

Oren Kurland's 3 most productive colleagues in number of publications:

David Carmel:26
Donald Metzler:23
Lillian Lee:10
 
 
 

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Oren Kurland

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Publications by Oren Kurland (bibliography)

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2012
 
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Hummel, Shay, Shtok, Anna, Raiber, Fiana, Kurland, Oren and Carmel, David (2012): Clarity re-visited. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 1039-1040.

We present a novel interpretation of Clarity [5], a widely used query performance predictor. While Clarity is commonly described as a measure of the "distance" between the language model of the top-retrieved documents and that of the collection, we show that it actually quantifies an additional property of the result list, namely, its diversity. This analysis, along with empirical evaluation, helps to explain the low prediction quality of Clarity for large-scale Web collections.

© All rights reserved Hummel et al. and/or ACM Press

 
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Khalaman, Savva and Kurland, Oren (2012): Utilizing inter-document similarities in federated search. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 1169-1170.

We demonstrate the merits of using inter-document similarities for federated search. Specifically, we study a results merging method that utilizes information induced from clusters of similar documents created across the lists retrieved from the collections. The method significantly outperforms state-of-the-art results merging approaches.

© All rights reserved Khalaman and Kurland and/or ACM Press

 
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Metzler, Donald and Kurland, Oren (2012): Experimental methods for information retrieval. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 1185-1186.

 
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Carmel, David and Kurland, Oren (2012): Query performance prediction for IR. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 1196-1197.

The goal of this tutorial is to expose participants to current research on query performance prediction. Participants will become familiar with state-of-the-art performance prediction methods, with common evaluation methodologies of prediction quality, and with potential applications that can utilize performance predictors. In addition, some open issues and challenges in the field will be discussed. This tutorial is an updated version of the SIGIR 2010 tutorial presented by David Carmel and Elad Yom-Tov on the same subject. This year we intend to expand on new results in the field, in particular focusing on recently developed frameworks that provide a unified model for performance prediction.

© All rights reserved Carmel and Kurland and/or ACM Press

2011
 
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Kozorovitsky, Anna Khudyak and Kurland, Oren (2011): Cluster-based fusion of retrieved lists. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 893-902.

Methods for fusing document lists that were retrieved in response to a query often use retrieval scores (or ranks) of documents in the lists. We present a novel probabilistic fusion approach that utilizes an additional source of rich information, namely, inter-document similarities. Specifically, our model integrates information induced from clusters of similar documents created across the lists with that produced by some fusion method that relies on retrieval scores (ranks). Empirical evaluation shows that our approach is highly effective for fusion. For example, the performance of our model is consistently better than that of the standard (effective) fusion method that it integrates. The performance also transcends that of standard fusion of re-ranked lists, where list re-ranking is based on clusters created from documents in the list.

© All rights reserved Kozorovitsky and Kurland and/or ACM Press

 
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Krikon, Eyal and Kurland, Oren (2011): Utilizing minimal relevance feedback for ad hoc retrieval. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 1099-1100.

Using relevance feedback can significantly improve (ad hoc) retrieval effectiveness. Yet, if little feedback is available, effectively exploiting it is a challenge. To that end, we present a novel approach that utilizes document passages. Empirical evaluation demonstrates the merits of the approach.

© All rights reserved Krikon and Kurland and/or ACM Press

2010
 
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Shtok, Anna, Kurland, Oren and Carmel, David (2010): Using statistical decision theory and relevance models for query-performance prediction. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2010. pp. 259-266.

We present a novel framework for the query-performance prediction task. That is, estimating the effectiveness of a search performed in response to a query in lack of relevance judgments. Our approach is based on using statistical decision theory for estimating the utility that a document ranking provides with respect to an information need expressed by the query. To address the uncertainty in inferring the information need, we estimate utility by the expected similarity between the given ranking and those induced by relevance models; the impact of a relevance model is based on its presumed representativeness of the information need. Specific query-performance predictors instantiated from the framework substantially outperform state-of-the-art predictors over five TREC corpora.

© All rights reserved Shtok et al. and/or their publisher

 
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Kurland, Oren and Lee, Lillian (2010): PageRank without hyperlinks: Structural reranking using links induced by language models. In ACM Transactions on Information Systems, 28 (4) p. 18.

The ad hoc retrieval task is to find documents in a corpus that are relevant to a query. Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural reranking approach to ad-hoc retrieval that applies to settings with no hyperlink information. We reorder the documents in an initially retrieved set by exploiting implicit asymmetric relationships among them. We consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another. We study a number of reranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks; the best resultant performance is comparable, and often superior, to that of a state-of-the-art pseudo-feedback-based retrieval approach. In addition, we demonstrate the merits of our language-model-based method for inducing interdocument links by comparing it to previously suggested notions of interdocument similarities (e.g., cosines within the vector-space model). We also show that our methods for inducing centrality are substantially more effective than approaches based on document-specific characteristics, several of which are novel to this study.

© All rights reserved Kurland and Lee and/or ACM Press

2009
 
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Kalmanovich, Inna Gelfer and Kurland, Oren (2009): Cluster-based query expansion. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2009. pp. 646-647.

We demonstrate the merits of using document clusters that are created offline to improve the overall effectiveness and performance robustness of a state-of-the-art pseudo-feedback-based query expansion method -- the relevance model.

© All rights reserved Kalmanovich and Kurland and/or their publisher

 
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Meister, Lior, Kurland, Oren and Kalmanovich, Inna Gelfer (2009): Integrating clusters created offline with query-specific clusters for document retrieval. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2009. pp. 706-707.

Previous work on cluster-based document retrieval has used either static document clusters that are created offline, or query-specific (dynamic) document clusters that are created from top-retrieved documents. We present the potential merit of integrating these two types of clusters.

© All rights reserved Meister et al. and/or their publisher

 
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Kurland, Oren and Lee, Lillian (2009): Clusters, language models, and ad hoc information retrieval. In ACM Transactions on Information Systems, 27 (3) p. 13.

The language-modeling approach to information retrieval provides an effective statistical framework for tackling various problems and often achieves impressive empirical performance. However, most previous work on language models for information retrieval focused on document-specific characteristics, and therefore did not take into account the structure of the surrounding corpus, a potentially rich source of additional information. We propose a novel algorithmic framework in which information provided by document-based language models is enhanced by the incorporation of information drawn from clusters of similar documents. Using this framework, we develop a suite of new algorithms. Even the simplest typically outperforms the standard language-modeling approach in terms of mean average precision (MAP) and recall, and our new interpolation algorithm posts statistically significant performance improvements for both metrics over all six corpora tested. An important aspect of our work is the way we model corpus structure. In contrast to most previous work on cluster-based retrieval that partitions the corpus, we demonstrate the effectiveness of a simple strategy based on a nearest-neighbors approach that produces overlapping clusters.

© All rights reserved Kurland and Lee and/or ACM Press

2008
 
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Kurland, Oren (2008): The opposite of smoothing: a language model approach to ranking query-specific document clusters. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 171-178.

Exploiting information induced from (query-specific) clustering of top-retrieved documents has long been proposed as means for improving precision at the very top ranks of the returned results. We present a novel language model approach to ranking query-specific clusters by the presumed percentage of relevant documents that they contain. While most previous cluster ranking approaches focus on the cluster as a whole, our model also exploits information induced from documents associated with the cluster. Our model substantially outperforms previous approaches for identifying clusters containing a high relevant-document percentage. Furthermore, using the model to produce document ranking yields precision-at-top-ranks performance that is consistently better than that of the initial ranking upon which clustering is performed; the performance also favorably compares with that of a state-of-the-art pseudo-feedback retrieval method.

© All rights reserved Kurland and/or ACM Press

 
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Kurland, Oren and Domshlak, Carmel (2008): A rank-aggregation approach to searching for optimal query-specific clusters. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 547-554.

To improve the precision at the very top ranks of a document list presented in response to a query, researchers suggested to exploit information induced from clustering of documents highly ranked by some initial search. We propose a novel model for ranking such (query-specific) clusters by the presumed percentage of relevant documents that they contain. The model is based on (i) proposing a palette of "witness" cluster properties that purportedly correlate with this percentage, (ii) devising concrete quantitative measures for these properties, and (iii) ordering the clusters via aggregation of rankings induced by these individual measures. Empirical evaluation shows that our model is consistently more effective than previously suggested methods in detecting clusters containing a high relevant-document percentage. Furthermore, the precision-at-top-ranks performance of this model transcends that of standard document-based retrieval, and competes with that of a state-of-the-art document-based retrieval approach.

© All rights reserved Kurland and Domshlak and/or ACM Press

 
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Zighelnic, Liron and Kurland, Oren (2008): Query-drift prevention for robust query expansion. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 825-826.

Pseudo-feedback-based automatic query expansion yields effective retrieval performance on average, but results in performance inferior to that of using the original query for many information needs. We address an important cause of this robustness issue, namely, the query drift problem, by fusing the results retrieved in response to the original query and to its expanded form. Our approach posts performance that is significantly better than that of retrieval based only on the original query and more robust than that of retrieval using the expanded query.

© All rights reserved Zighelnic and Kurland and/or ACM Press

 
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Bendersky, Michael and Kurland, Oren (2008): Re-ranking search results using document-passage graphs. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 853-854.

We present a novel passage-based approach to re-ranking documents in an initially retrieved list so as to improve precision at top ranks. While most work on passage-based document retrieval ranks a document based on the query similarity of its constituent passages, our approach leverages information about the centrality of the document passages with respect to the initial document list. Passage centrality is induced over a bipartite document-passage graph, wherein edge weights represent document-passage similarities. Empirical evaluation shows that our approach yields effective re-ranking performance. Furthermore, the performance is superior to that of previously proposed passage-based document ranking methods.

© All rights reserved Bendersky and Kurland and/or ACM Press

 
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Bendersky, Michael and Kurland, Oren (2008): Utilizing Passage-Based Language Models for Document Retrieval. In: Macdonald, Craig, Ounis, Iadh, Plachouras, Vassilis, Ruthven, Ian and White, Ryen W. (eds.) Advances in Information Retrieval - 30th European Conference on IR Research - ECIR 2008 March 30-April 3, 2008, Glasgow, UK. pp. 162-174.

2007
 
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Winaver, Mattan, Kurland, Oren and Domshlak, Carmel (2007): Towards robust query expansion: model selection in the language modeling framework. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 729-730.

We propose a language-model-based approach for addressing the performance robustness problem -- with respect to free-parameters' values -- of pseudo-feedback-based query-expansion methods. Given a query, we create a set of language models representing different forms of its expansion by varying the parameters' values of some expansion method; then, we select a single model using criteria originally proposed for evaluating the performance of using the original query, or for deciding whether to employ expansion at all. Experimental results show that these criteria are highly effective in selecting relevance language models that are not only significantly more effective than poor performing ones, but that also yield performance that is almost indistinguishable from that of manually optimized relevance models.

© All rights reserved Winaver et al. and/or ACM Press

2006
 
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Kurland, Oren and Lee, Lillian (2006): Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006. pp. 83-90.

We present an approach to improving the precision of an initial document ranking wherein we utilize cluster information within a graph-based framework. The main idea is to perform reranking based on centrality within bipartite graphs of documents (on one side) and clusters (on the other side), on the premise that these are mutually reinforcing entities. Links between entities are created via consideration of language models induced from them. We find that our cluster-document graphs give rise to much better retrieval performance than previously proposed document-only graphs do. For example, authority-based reranking of documents via a HITS-style cluster-based approach outperforms a previously-proposed PageRank-inspired algorithm applied to solely-document graphs. Moreover, we also show that computing authority scores for clusters constitutes an effective method for identifying clusters containing a large percentage of relevant documents.

© All rights reserved Kurland and Lee and/or ACM Press

2005
 
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Kurland, Oren, Lee, Lillian and Domshlak, Carmel (2005): Better than the real thing?: iterative pseudo-query processing using cluster-based language models. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 19-26.

We present a novel approach to pseudo-feedback-based ad hoc retrieval that uses language models induced from both documents and clusters. First, we treat the pseudo-feedback documents produced in response to the original query as a set of pseudo-query that themselves can serve as input to the retrieval process. Observing that the documents returned in response to the pseudo-query can then act as pseudo-query for subsequent rounds, we arrive at a formulation of pseudo-query-based retrieval as an iterative process. Experiments show that several concrete instantiations of this idea, when applied in conjunction with techniques designed to heighten precision, yield performance results rivaling those of a number of previously-proposed algorithms, including the standard language-modeling approach. The use of cluster-based language models is a key contributing factor to our algorithms' success.

© All rights reserved Kurland et al. and/or ACM Press

 
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Kurland, Oren and Lee, Lillian (2005): PageRank without hyperlinks: structural re-ranking using links induced by language models. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 306-313.

Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural re-ranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another; in doing so, we take care to prevent bias against long documents. We study a number of re-ranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks.

© All rights reserved Kurland and Lee and/or ACM Press

2004
 
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Kurland, Oren and Lee, Lillian (2004): Corpus structure, language models, and ad hoc information retrieval. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 194-201.

Most previous work on the recently developed language-modeling approach to information retrieval focuses on document-specific characteristics, and therefore does not take into account the structure of the surrounding corpus. We propose a novel algorithmic framework in which information provided by document-based language models is enhanced by the incorporation of information drawn from clusters of similar documents. Using this framework, we develop a suite of new algorithms. Even the simplest typically outperforms the standard language-modeling approach in precision and recall, and our new interpolation algorithm posts statistically significant improvements for both metrics over all three corpora tested.

© All rights reserved Kurland and Lee and/or ACM Press

 
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Page Information

Page maintainer: The Editorial Team
URL: http://www.interaction-design.org/references/authors/oren_kurland.html

Publication statistics

Pub. period:2004-2012
Pub. count:21
Number of co-authors:15



Co-authors

Number of publications with 3 favourite co-authors:

Lillian Lee:6
David Carmel:3
Carmel Domshlak:3

 

 

Productive colleagues

Oren Kurland's 3 most productive colleagues in number of publications:

David Carmel:26
Donald Metzler:23
Lillian Lee:10
 
 
 

Upcoming Courses

go to course
User-Centred Design - Module 3
69% booked. Starts in 26 days
 
 

Featured chapter

Marc Hassenzahl explains the fascinating concept of User Experience and Experience Design. Commentaries by Don Norman, Eric Reiss, Mark Blythe, and Whitney Hess

User Experience and Experience Design !

 
 

Our Latest Books

 
 
The Social Design of Technical Systems: Building technologies for communities. 2nd Edition
by Brian Whitworth and Adnan Ahmad
start reading
 
 
 
 
Gamification at Work: Designing Engaging Business Software
by Janaki Mythily Kumar and Mario Herger
start reading
 
 
 
 
The Social Design of Technical Systems: Building technologies for communities
by Brian Whitworth and Adnan Ahmad
start reading
 
 
 
 
The Encyclopedia of Human-Computer Interaction, 2nd Ed.
by Mads Soegaard and Rikke Friis Dam
start reading