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description Publicationkeyboard_double_arrow_right Article 2015Embargo end date: 01 Jan 2015Publisher:arXiv Funded by:NWO | SPuDisc: Searching Public..., NWO | Digging archaeology data:..., NWO | Semantic Search in E-Disc... +3 projectsNWO| SPuDisc: Searching Public Discourse ,NWO| Digging archaeology data: image search and markup (DADAISM) ,NWO| Semantic Search in E-Discovery ,EC| LIMOSINE ,NWO| Modeling and Learning from Implicit Feedback in Information Retrieval ,EC| VOX-POLAuthors: Chuklin, Aleksandr; de Rijke, Maarten;Chuklin, Aleksandr; de Rijke, Maarten;Currently, the quality of a search engine is often determined using so-called topical relevance, i.e., the match between the user intent (expressed as a query) and the content of the document. In this work we want to draw attention to two aspects of retrieval system performance affected by the presentation of results: result attractiveness ("perceived relevance") and immediate usefulness of the snippets ("snippet relevance"). Perceived relevance may influence discoverability of good topical documents and seemingly better rankings may in fact be less useful to the user if good-looking snippets lead to irrelevant documents or vice-versa. And result items on a search engine result page (SERP) with high snippet relevance may add towards the total utility gained by the user even without the need to click those items. We start by motivating the need to collect different aspects of relevance (topical, perceived and snippet relevances) and how these aspects can improve evaluation measures. We then discuss possible ways to collect these relevance aspects using crowdsourcing and the challenges arising from that. Comment: SIGIR 2014 Workshop on Gathering Efficient Assessments of Relevance
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2015Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2015License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1501.06412&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2015Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2015License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1501.06412&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2013Embargo end date: 01 Jan 2013 NetherlandsPublisher:arXiv Funded by:NWO | Semantic Search in E-Disc..., NWO | Modeling and Learning fro..., NWO | Building Rich Links to En... +4 projectsNWO| Semantic Search in E-Discovery ,NWO| Modeling and Learning from Implicit Feedback in Information Retrieval ,NWO| Building Rich Links to Enable Television History Research ,EC| COMPLACS ,EC| LIMOSINE ,NWO| SPuDisc: Searching Public Discourse ,NWO| Digging archaeology data: image search and markup (DADAISM)Authors: Zoghi, Masrour; Whiteson, Shimon; Munos, Remi; de Rijke, Maarten;Zoghi, Masrour; Whiteson, Shimon; Munos, Remi; de Rijke, Maarten;handle: 11245/1.430126
This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a finite-time regret bound of order O(log t). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art. Comment: 13 pages, 6 figures
NARCIS arrow_drop_down NARCISArticle . 2014arXiv.org e-Print ArchiveOther literature type . Preprint . 2013Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2013License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1312.3393&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert NARCIS arrow_drop_down NARCISArticle . 2014arXiv.org e-Print ArchiveOther literature type . Preprint . 2013Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2013License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1312.3393&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Article 2015Embargo end date: 01 Jan 2015Publisher:arXiv Funded by:NWO | SPuDisc: Searching Public..., NWO | Digging archaeology data:..., NWO | Semantic Search in E-Disc... +3 projectsNWO| SPuDisc: Searching Public Discourse ,NWO| Digging archaeology data: image search and markup (DADAISM) ,NWO| Semantic Search in E-Discovery ,EC| LIMOSINE ,NWO| Modeling and Learning from Implicit Feedback in Information Retrieval ,EC| VOX-POLAuthors: Chuklin, Aleksandr; de Rijke, Maarten;Chuklin, Aleksandr; de Rijke, Maarten;Currently, the quality of a search engine is often determined using so-called topical relevance, i.e., the match between the user intent (expressed as a query) and the content of the document. In this work we want to draw attention to two aspects of retrieval system performance affected by the presentation of results: result attractiveness ("perceived relevance") and immediate usefulness of the snippets ("snippet relevance"). Perceived relevance may influence discoverability of good topical documents and seemingly better rankings may in fact be less useful to the user if good-looking snippets lead to irrelevant documents or vice-versa. And result items on a search engine result page (SERP) with high snippet relevance may add towards the total utility gained by the user even without the need to click those items. We start by motivating the need to collect different aspects of relevance (topical, perceived and snippet relevances) and how these aspects can improve evaluation measures. We then discuss possible ways to collect these relevance aspects using crowdsourcing and the challenges arising from that. Comment: SIGIR 2014 Workshop on Gathering Efficient Assessments of Relevance
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2015Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2015License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1501.06412&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2015Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2015License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1501.06412&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2013Embargo end date: 01 Jan 2013 NetherlandsPublisher:arXiv Funded by:NWO | Semantic Search in E-Disc..., NWO | Modeling and Learning fro..., NWO | Building Rich Links to En... +4 projectsNWO| Semantic Search in E-Discovery ,NWO| Modeling and Learning from Implicit Feedback in Information Retrieval ,NWO| Building Rich Links to Enable Television History Research ,EC| COMPLACS ,EC| LIMOSINE ,NWO| SPuDisc: Searching Public Discourse ,NWO| Digging archaeology data: image search and markup (DADAISM)Authors: Zoghi, Masrour; Whiteson, Shimon; Munos, Remi; de Rijke, Maarten;Zoghi, Masrour; Whiteson, Shimon; Munos, Remi; de Rijke, Maarten;handle: 11245/1.430126
This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a finite-time regret bound of order O(log t). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art. Comment: 13 pages, 6 figures
NARCIS arrow_drop_down NARCISArticle . 2014arXiv.org e-Print ArchiveOther literature type . Preprint . 2013Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2013License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1312.3393&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert NARCIS arrow_drop_down NARCISArticle . 2014arXiv.org e-Print ArchiveOther literature type . Preprint . 2013Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2013License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1312.3393&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu