狠狠撸shows by User: soulierlaure

狠狠撸shows by User: soulierlaure / http://www.slideshare.net/images/logo.gif 狠狠撸shows by User: soulierlaure / Tue, 25 Oct 2016 20:43:18 GMT 狠狠撸Share feed for 狠狠撸shows by User: soulierlaure Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration /slideshow/answering-twitter-questions-a-model-for-recommending-answerers-through-social-collaboration/67646571 recocoll-161025204319
Previous studies on the practice of asking questions on social networking sites have shown that most questions remain unanswered and that most of the replies, if any, are only from members of the questioner's neighborhood. In this paper, we specifically consider the challenging task of solving a question posted on Twitter. The latter generally remains unanswered and most of the replies, if any, are only from members of the questioner's neighborhood. As outlined in previous work related to community Q\&A, we believe that question-answering is a collaborative process and that the relevant answer to a question post is an aggregation of answer nuggets posted by a group of relevant users. Thus, the problem of identifying the relevant answer turns into the problem of identifying the right group of users who would provide useful answers and would possibly be willing to collaborate together in the long-term. Accordingly, we present a novel method, called CRAQ, that is built on the collaboration paradigm and formulated as a group entropy optimization problem. To optimize the quality of the group, an information gain measure is used to select the most likely ``informative" users according to topical and collaboration likelihood predictive features. Crowd-based experiments performed on two crisis-related Twitter datasets demonstrate the effectiveness of our collaborative-based answering approach.]]>
Previous studies on the practice of asking questions on social networking sites have shown that most questions remain unanswered and that most of the replies, if any, are only from members of the questioner's neighborhood. In this paper, we specifically consider the challenging task of solving a question posted on Twitter. The latter generally remains unanswered and most of the replies, if any, are only from members of the questioner's neighborhood. As outlined in previous work related to community Q\&A, we believe that question-answering is a collaborative process and that the relevant answer to a question post is an aggregation of answer nuggets posted by a group of relevant users. Thus, the problem of identifying the relevant answer turns into the problem of identifying the right group of users who would provide useful answers and would possibly be willing to collaborate together in the long-term. Accordingly, we present a novel method, called CRAQ, that is built on the collaboration paradigm and formulated as a group entropy optimization problem. To optimize the quality of the group, an information gain measure is used to select the most likely ``informative" users according to topical and collaboration likelihood predictive features. Crowd-based experiments performed on two crisis-related Twitter datasets demonstrate the effectiveness of our collaborative-based answering approach.]]> Tue, 25 Oct 2016 20:43:18 GMT /slideshow/answering-twitter-questions-a-model-for-recommending-answerers-through-social-collaboration/67646571 soulierlaure@slideshare.net(soulierlaure) Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration soulierlaure Previous studies on the practice of asking questions on social networking sites have shown that most questions remain unanswered and that most of the replies, if any, are only from members of the questioner's neighborhood. In this paper, we specifically consider the challenging task of solving a question posted on Twitter. The latter generally remains unanswered and most of the replies, if any, are only from members of the questioner's neighborhood. As outlined in previous work related to community Q\&A, we believe that question-answering is a collaborative process and that the relevant answer to a question post is an aggregation of answer nuggets posted by a group of relevant users. Thus, the problem of identifying the relevant answer turns into the problem of identifying the right group of users who would provide useful answers and would possibly be willing to collaborate together in the long-term. Accordingly, we present a novel method, called CRAQ, that is built on the collaboration paradigm and formulated as a group entropy optimization problem. To optimize the quality of the group, an information gain measure is used to select the most likely ``informative" users according to topical and collaboration likelihood predictive features. Crowd-based experiments performed on two crisis-related Twitter datasets demonstrate the effectiveness of our collaborative-based answering approach. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/recocoll-161025204319-thumbnail.jpg?width=120&height=120&fit=bounds" /> Previous studies on the practice of asking questions on social networking sites have shown that most questions remain unanswered and that most of the replies, if any, are only from members of the questioner's neighborhood. In this paper, we specifically consider the challenging task of solving a question posted on Twitter. The latter generally remains unanswered and most of the replies, if any, are only from members of the questioner's neighborhood. As outlined in previous work related to community Q\&A, we believe that question-answering is a collaborative process and that the relevant answer to a question post is an aggregation of answer nuggets posted by a group of relevant users. Thus, the problem of identifying the relevant answer turns into the problem of identifying the right group of users who would provide useful answers and would possibly be willing to collaborate together in the long-term. Accordingly, we present a novel method, called CRAQ, that is built on the collaboration paradigm and formulated as a group entropy optimization problem. To optimize the quality of the group, an information gain measure is used to select the most likely ``informative" users according to topical and collaboration likelihood predictive features. Crowd-based experiments performed on two crisis-related Twitter datasets demonstrate the effectiveness of our collaborative-based answering approach.

Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration from UPMC - Sorbonne Universities

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0 Collaborative Information Retrieval: Frameworks, Theoretical Models and Emerging Topics - Tutorial at ICTIR 2016 /slideshow/collaborative-information-retrieval-frameworks-theoretical-models-and-emerging-topics-tutorial-at-ictir-2016/67309877 tuto-full-v1-161017194300
A great amount of research in the IR domain mostly dealt with both the design of enhanced document ranking models allowing search improvement through user-to-system collaboration. However, in addition to user-to-system form of collaboration, user-to-user collaboration is increasingly acknowledged as an effective mean for gathering the complementary skills and/or knowledge of individual users in order to solve complex search tasks.This tutorial will first give an overview of the ways into collaboration has been implemented in IR models with the attempt of improving the search outcomes with respect to several tasks and related frameworks (ad-hoc search, group-based recommendation, social search, collaborative search). Second, as envisioned in collaborative IR domain (CIR), we will focus on the theoretical models that support and drive user-to-user collaboration in order to perform shared IR tasks. Third, we will develop a road map on emerging and relevant topics addressing issues related to collaboration design. Our goal is to provide participants with concepts and motivation allowing them to investigate this emerging IR domain as well as giving them some clues on how to tackle issues related to the optimization of collaborative tasks. More specifically, the tutorial aims to: 1. Give an overview of the key concept of collaboration in IR and related research topics; 2. Present state-of-the art CIR techniques and models; 3. Discuss about the emerging topics that deal with collaboration; 4. Point out some challenges ahead. ]]>
A great amount of research in the IR domain mostly dealt with both the design of enhanced document ranking models allowing search improvement through user-to-system collaboration. However, in addition to user-to-system form of collaboration, user-to-user collaboration is increasingly acknowledged as an effective mean for gathering the complementary skills and/or knowledge of individual users in order to solve complex search tasks.This tutorial will first give an overview of the ways into collaboration has been implemented in IR models with the attempt of improving the search outcomes with respect to several tasks and related frameworks (ad-hoc search, group-based recommendation, social search, collaborative search). Second, as envisioned in collaborative IR domain (CIR), we will focus on the theoretical models that support and drive user-to-user collaboration in order to perform shared IR tasks. Third, we will develop a road map on emerging and relevant topics addressing issues related to collaboration design. Our goal is to provide participants with concepts and motivation allowing them to investigate this emerging IR domain as well as giving them some clues on how to tackle issues related to the optimization of collaborative tasks. More specifically, the tutorial aims to: 1. Give an overview of the key concept of collaboration in IR and related research topics; 2. Present state-of-the art CIR techniques and models; 3. Discuss about the emerging topics that deal with collaboration; 4. Point out some challenges ahead. ]]> Mon, 17 Oct 2016 19:43:00 GMT /slideshow/collaborative-information-retrieval-frameworks-theoretical-models-and-emerging-topics-tutorial-at-ictir-2016/67309877 soulierlaure@slideshare.net(soulierlaure) Collaborative Information Retrieval: Frameworks, Theoretical Models and Emerging Topics - Tutorial at ICTIR 2016 soulierlaure A great amount of research in the IR domain mostly dealt with both the design of enhanced document ranking models allowing search improvement through user-to-system collaboration. However, in addition to user-to-system form of collaboration, user-to-user collaboration is increasingly acknowledged as an effective mean for gathering the complementary skills and/or knowledge of individual users in order to solve complex search tasks.This tutorial will first give an overview of the ways into collaboration has been implemented in IR models with the attempt of improving the search outcomes with respect to several tasks and related frameworks (ad-hoc search, group-based recommendation, social search, collaborative search). Second, as envisioned in collaborative IR domain (CIR), we will focus on the theoretical models that support and drive user-to-user collaboration in order to perform shared IR tasks. Third, we will develop a road map on emerging and relevant topics addressing issues related to collaboration design. Our goal is to provide participants with concepts and motivation allowing them to investigate this emerging IR domain as well as giving them some clues on how to tackle issues related to the optimization of collaborative tasks. More specifically, the tutorial aims to: 1. Give an overview of the key concept of collaboration in IR and related research topics; 2. Present state-of-the art CIR techniques and models; 3. Discuss about the emerging topics that deal with collaboration; 4. Point out some challenges ahead. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tuto-full-v1-161017194300-thumbnail.jpg?width=120&height=120&fit=bounds" /> A great amount of research in the IR domain mostly dealt with both the design of enhanced document ranking models allowing search improvement through user-to-system collaboration. However, in addition to user-to-system form of collaboration, user-to-user collaboration is increasingly acknowledged as an effective mean for gathering the complementary skills and/or knowledge of individual users in order to solve complex search tasks.This tutorial will first give an overview of the ways into collaboration has been implemented in IR models with the attempt of improving the search outcomes with respect to several tasks and related frameworks (ad-hoc search, group-based recommendation, social search, collaborative search). Second, as envisioned in collaborative IR domain (CIR), we will focus on the theoretical models that support and drive user-to-user collaboration in order to perform shared IR tasks. Third, we will develop a road map on emerging and relevant topics addressing issues related to collaboration design. Our goal is to provide participants with concepts and motivation allowing them to investigate this emerging IR domain as well as giving them some clues on how to tackle issues related to the optimization of collaborative tasks. More specifically, the tutorial aims to: 1. Give an overview of the key concept of collaboration in IR and related research topics; 2. Present state-of-the art CIR techniques and models; 3. Discuss about the emerging topics that deal with collaboration; 4. Point out some challenges ahead.

Collaborative Information Retrieval: Frameworks, Theoretical Models and Emerging Topics - Tutorial at ICTIR 2016 from UPMC - Sorbonne Universities

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0 Collaborative Information Retrieval: Concepts, Models and Evaluation /slideshow/collaborative-information-retrieval-concepts-models-and-evaluation/60798036 v3-160412080248
CIR Tutorial @ ACIR 2016]]>
CIR Tutorial @ ACIR 2016]]> Tue, 12 Apr 2016 08:02:48 GMT /slideshow/collaborative-information-retrieval-concepts-models-and-evaluation/60798036 soulierlaure@slideshare.net(soulierlaure) Collaborative Information Retrieval: Concepts, Models and Evaluation soulierlaure CIR Tutorial @ ACIR 2016 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/v3-160412080248-thumbnail.jpg?width=120&height=120&fit=bounds" /> CIR Tutorial @ ACIR 2016

Collaborative Information Retrieval: Concepts, Models and Evaluation from UPMC - Sorbonne Universities

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0 Thesis slides - Definition and evluation of collaborative information retrieval models based on users' domain expertise and roles /slideshow/thesis-slides-definition-and-evluation-of-collaborative-information-retrieval-models-based-on-users-domain-expertise-and-roles/54485785 2014presentationthesesoulier-151028161505-lva1-app6891
The research topic of this document deals with a particular setting of information retrieval (IR), referred to as collaborative information retrieval (CIR), in which a set of multiple collaborators share the same information need. Collaboration is particularly used in case of complex tasks in which an individual user may have insufficient knowledge and may benefit from the expertise/knowledge or complementarity of other collaborators. This multi-user context rises several challenges in terms of search interfaces as well as ranking models, since new paradigms must be considered, namely division of labor, sharing of knowledge and awareness. These paradigms aim at avoiding redundancy between collaborators in order to reach a synergic effect within the collaboration process. Several approaches have been proposed in the literature. First, search interfaces have been oriented towards a user mediation in order to support collaborators’ actions through information storage or communication tools. Second, more close to our contributions, previous work focus on the information access issue by designing ranking models adapted to collaborative environments dealing with the challenges of (1) personalizing result set to collaborators, (2) favoring the sharing of knowledge, (3) dividing the labor among collaborators and/or (4) considering particular roles of collaborators within the information seeking process. In this thesis, we focus, more particularly, on two main aspects of the collaboration : — The expertise of collaborators by proposing retrieval models adapted to the domain expertise level of collaborators. The expertise levels might be vertical, in the case of domain expert and novice, or horizontal when collaborators have different subdomain expertise. We, therefore, propose two CIR models on two steps including a document relevance scoring with respect to each role and a document allocation to user roles through the Expectation–Maximization (EM) learning method applied on the document relevance scoring in order to assign documents to the most likely suited user. — The complementarity of collaborators throughout the information seeking process by mining their roles on the assumptions that collaborators might be different and complementary in some skills. We propose two algorithms based either on predefined roles or latent roles which (1) learns about the roles of the collaborators using various search-related features for each individual involved in the search session, and (2) adapts the document ranking to the mined roles of collaborators. ]]>
The research topic of this document deals with a particular setting of information retrieval (IR), referred to as collaborative information retrieval (CIR), in which a set of multiple collaborators share the same information need. Collaboration is particularly used in case of complex tasks in which an individual user may have insufficient knowledge and may benefit from the expertise/knowledge or complementarity of other collaborators. This multi-user context rises several challenges in terms of search interfaces as well as ranking models, since new paradigms must be considered, namely division of labor, sharing of knowledge and awareness. These paradigms aim at avoiding redundancy between collaborators in order to reach a synergic effect within the collaboration process. Several approaches have been proposed in the literature. First, search interfaces have been oriented towards a user mediation in order to support collaborators’ actions through information storage or communication tools. Second, more close to our contributions, previous work focus on the information access issue by designing ranking models adapted to collaborative environments dealing with the challenges of (1) personalizing result set to collaborators, (2) favoring the sharing of knowledge, (3) dividing the labor among collaborators and/or (4) considering particular roles of collaborators within the information seeking process. In this thesis, we focus, more particularly, on two main aspects of the collaboration : — The expertise of collaborators by proposing retrieval models adapted to the domain expertise level of collaborators. The expertise levels might be vertical, in the case of domain expert and novice, or horizontal when collaborators have different subdomain expertise. We, therefore, propose two CIR models on two steps including a document relevance scoring with respect to each role and a document allocation to user roles through the Expectation–Maximization (EM) learning method applied on the document relevance scoring in order to assign documents to the most likely suited user. — The complementarity of collaborators throughout the information seeking process by mining their roles on the assumptions that collaborators might be different and complementary in some skills. We propose two algorithms based either on predefined roles or latent roles which (1) learns about the roles of the collaborators using various search-related features for each individual involved in the search session, and (2) adapts the document ranking to the mined roles of collaborators. ]]> Wed, 28 Oct 2015 16:15:05 GMT /slideshow/thesis-slides-definition-and-evluation-of-collaborative-information-retrieval-models-based-on-users-domain-expertise-and-roles/54485785 soulierlaure@slideshare.net(soulierlaure) Thesis slides - Definition and evluation of collaborative information retrieval models based on users' domain expertise and roles soulierlaure The research topic of this document deals with a particular setting of information retrieval (IR), referred to as collaborative information retrieval (CIR), in which a set of multiple collaborators share the same information need. Collaboration is particularly used in case of complex tasks in which an individual user may have insufficient knowledge and may benefit from the expertise/knowledge or complementarity of other collaborators. This multi-user context rises several challenges in terms of search interfaces as well as ranking models, since new paradigms must be considered, namely division of labor, sharing of knowledge and awareness. These paradigms aim at avoiding redundancy between collaborators in order to reach a synergic effect within the collaboration process. Several approaches have been proposed in the literature. First, search interfaces have been oriented towards a user mediation in order to support collaborators’ actions through information storage or communication tools. Second, more close to our contributions, previous work focus on the information access issue by designing ranking models adapted to collaborative environments dealing with the challenges of (1) personalizing result set to collaborators, (2) favoring the sharing of knowledge, (3) dividing the labor among collaborators and/or (4) considering particular roles of collaborators within the information seeking process. In this thesis, we focus, more particularly, on two main aspects of the collaboration : — The expertise of collaborators by proposing retrieval models adapted to the domain expertise level of collaborators. The expertise levels might be vertical, in the case of domain expert and novice, or horizontal when collaborators have different subdomain expertise. We, therefore, propose two CIR models on two steps including a document relevance scoring with respect to each role and a document allocation to user roles through the Expectation–Maximization (EM) learning method applied on the document relevance scoring in order to assign documents to the most likely suited user. — The complementarity of collaborators throughout the information seeking process by mining their roles on the assumptions that collaborators might be different and complementary in some skills. We propose two algorithms based either on predefined roles or latent roles which (1) learns about the roles of the collaborators using various search-related features for each individual involved in the search session, and (2) adapts the document ranking to the mined roles of collaborators. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2014presentationthesesoulier-151028161505-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> The research topic of this document deals with a particular setting of information retrieval (IR), referred to as collaborative information retrieval (CIR), in which a set of multiple collaborators share the same information need. Collaboration is particularly used in case of complex tasks in which an individual user may have insufficient knowledge and may benefit from the expertise/knowledge or complementarity of other collaborators. This multi-user context rises several challenges in terms of search interfaces as well as ranking models, since new paradigms must be considered, namely division of labor, sharing of knowledge and awareness. These paradigms aim at avoiding redundancy between collaborators in order to reach a synergic effect within the collaboration process. Several approaches have been proposed in the literature. First, search interfaces have been oriented towards a user mediation in order to support collaborators’ actions through information storage or communication tools. Second, more close to our contributions, previous work focus on the information access issue by designing ranking models adapted to collaborative environments dealing with the challenges of (1) personalizing result set to collaborators, (2) favoring the sharing of knowledge, (3) dividing the labor among collaborators and/or (4) considering particular roles of collaborators within the information seeking process. In this thesis, we focus, more particularly, on two main aspects of the collaboration : — The expertise of collaborators by proposing retrieval models adapted to the domain expertise level of collaborators. The expertise levels might be vertical, in the case of domain expert and novice, or horizontal when collaborators have different subdomain expertise. We, therefore, propose two CIR models on two steps including a document relevance scoring with respect to each role and a document allocation to user roles through the Expectation–Maximization (EM) learning method applied on the document relevance scoring in order to assign documents to the most likely suited user. — The complementarity of collaborators throughout the information seeking process by mining their roles on the assumptions that collaborators might be different and complementary in some skills. We propose two algorithms based either on predefined roles or latent roles which (1) learns about the roles of the collaborators using various search-related features for each individual involved in the search session, and (2) adapts the document ranking to the mined roles of collaborators.

Thesis slides - Definition and evluation of collaborative information retrieval models based on users' domain expertise and roles from UPMC - Sorbonne Universities

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0 Understanding the Impact of the Role Factor in Collaborative Information Retrieval /slideshow/understanding-the-impact-of-the-role-factor-in-collaborative-information-retrieval/54485625 cikm2015-vfinal-151028161133-lva1-app6891
Collaborative information retrieval systems often rely on division of labor policies. Such policies allow work to be divided among collaborators with the aim of preventing redundancy and optimizing the synergic effects of collaboration. Most of the underlying methods achieve these goals by the means of explicit vs. implicit role-based mediation. In this paper, we investigate whether and how different factors, such as users' behavior, search strategies, and effectiveness, are related to role assignment within a collaborative exploratory search. Our main findings suggest that: (1) spontaneous and cohesive implicit roles might emerge during the collaborative search session implying users with no prior roles, and that these implicit roles favor the search precision, (2) role drift might occur alongside the search session performed by users with prior-assigned roles.]]>
Collaborative information retrieval systems often rely on division of labor policies. Such policies allow work to be divided among collaborators with the aim of preventing redundancy and optimizing the synergic effects of collaboration. Most of the underlying methods achieve these goals by the means of explicit vs. implicit role-based mediation. In this paper, we investigate whether and how different factors, such as users' behavior, search strategies, and effectiveness, are related to role assignment within a collaborative exploratory search. Our main findings suggest that: (1) spontaneous and cohesive implicit roles might emerge during the collaborative search session implying users with no prior roles, and that these implicit roles favor the search precision, (2) role drift might occur alongside the search session performed by users with prior-assigned roles.]]> Wed, 28 Oct 2015 16:11:33 GMT /slideshow/understanding-the-impact-of-the-role-factor-in-collaborative-information-retrieval/54485625 soulierlaure@slideshare.net(soulierlaure) Understanding the Impact of the Role Factor in Collaborative Information Retrieval soulierlaure Collaborative information retrieval systems often rely on division of labor policies. Such policies allow work to be divided among collaborators with the aim of preventing redundancy and optimizing the synergic effects of collaboration. Most of the underlying methods achieve these goals by the means of explicit vs. implicit role-based mediation. In this paper, we investigate whether and how different factors, such as users' behavior, search strategies, and effectiveness, are related to role assignment within a collaborative exploratory search. Our main findings suggest that: (1) spontaneous and cohesive implicit roles might emerge during the collaborative search session implying users with no prior roles, and that these implicit roles favor the search precision, (2) role drift might occur alongside the search session performed by users with prior-assigned roles. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cikm2015-vfinal-151028161133-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Collaborative information retrieval systems often rely on division of labor policies. Such policies allow work to be divided among collaborators with the aim of preventing redundancy and optimizing the synergic effects of collaboration. Most of the underlying methods achieve these goals by the means of explicit vs. implicit role-based mediation. In this paper, we investigate whether and how different factors, such as users' behavior, search strategies, and effectiveness, are related to role assignment within a collaborative exploratory search. Our main findings suggest that: (1) spontaneous and cohesive implicit roles might emerge during the collaborative search session implying users with no prior roles, and that these implicit roles favor the search precision, (2) role drift might occur alongside the search session performed by users with prior-assigned roles.

Understanding the Impact of the Role Factor in Collaborative Information Retrieval from UPMC - Sorbonne Universities

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0 Un modèle de recherche d’information collaborative basé sur l’expertise des utilisateurs https://fr.slideshare.net/slideshow/un-modle-de-recherche-dinformation-collaborative-bas-sur-lexpertise-des-utilisateurs/33262728 coria2014final-140408041653-phpapp02
Nous nous intéressons à un contexte de recherche d'information collaborative où les utilisateurs sont caractérisés par différents niveaux d'expertise du sujet de la requête. Nous proposons un modèle d'ordonnancement de documents intégrant les r?les d'expert et de novice tenant compte de la spécificité de chacun des r?les et assignant un document à l'utilisateur le plus à même de le juger en fonction de son niveau d'expertise. Pour cela, les scores de pertinence document-requête sont calculés en y intégrant la variable r?le puis optimisés par la méthode d'apprentissage de maximisation de l'espérance mathématique. L'évaluation expérimentale est réalisée selon un cadre de simulation de la collaboration à partir de fichiers log de la collection TREC Interactive et montre l'efficacité de notre approche.]]>
Nous nous intéressons à un contexte de recherche d'information collaborative où les utilisateurs sont caractérisés par différents niveaux d'expertise du sujet de la requête. Nous proposons un modèle d'ordonnancement de documents intégrant les r?les d'expert et de novice tenant compte de la spécificité de chacun des r?les et assignant un document à l'utilisateur le plus à même de le juger en fonction de son niveau d'expertise. Pour cela, les scores de pertinence document-requête sont calculés en y intégrant la variable r?le puis optimisés par la méthode d'apprentissage de maximisation de l'espérance mathématique. L'évaluation expérimentale est réalisée selon un cadre de simulation de la collaboration à partir de fichiers log de la collection TREC Interactive et montre l'efficacité de notre approche.]]> Tue, 08 Apr 2014 04:16:53 GMT https://fr.slideshare.net/slideshow/un-modle-de-recherche-dinformation-collaborative-bas-sur-lexpertise-des-utilisateurs/33262728 soulierlaure@slideshare.net(soulierlaure) Un modèle de recherche d’information collaborative basé sur l’expertise des utilisateurs soulierlaure Nous nous intéressons à un contexte de recherche d'information collaborative où les utilisateurs sont caractérisés par différents niveaux d'expertise du sujet de la requête. Nous proposons un modèle d'ordonnancement de documents intégrant les r?les d'expert et de novice tenant compte de la spécificité de chacun des r?les et assignant un document à l'utilisateur le plus à même de le juger en fonction de son niveau d'expertise. Pour cela, les scores de pertinence document-requête sont calculés en y intégrant la variable r?le puis optimisés par la méthode d'apprentissage de maximisation de l'espérance mathématique. L'évaluation expérimentale est réalisée selon un cadre de simulation de la collaboration à partir de fichiers log de la collection TREC Interactive et montre l'efficacité de notre approche. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/coria2014final-140408041653-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Nous nous intéressons à un contexte de recherche d'information collaborative où les utilisateurs sont caractérisés par différents niveaux d'expertise du sujet de la requête. Nous proposons un modèle d'ordonnancement de documents intégrant les r?les d'expert et de novice tenant compte de la spécificité de chacun des r?les et assignant un document à l'utilisateur le plus à même de le juger en fonction de son niveau d'expertise. Pour cela, les scores de pertinence document-requête sont calculés en y intégrant la variable r?le puis optimisés par la méthode d'apprentissage de maximisation de l'espérance mathématique. L'évaluation expérimentale est réalisée selon un cadre de simulation de la collaboration à partir de fichiers log de la collection TREC Interactive et montre l'efficacité de notre approche.

from UPMC - Sorbonne Universities

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0 A Collaborative Document Ranking Model for a Multi-Faceted Search /slideshow/a-collaborative-document-ranking-model-for-a-multifaceted-search-29313464/29313464 airsv0-131218021854-phpapp02
This slides presents a novel collaborative document ranking model which aims at solving a complex information retrieval task involving a multi-faceted information need. For this purpose, we consider a group of users, viewed as experts, who collaborate by addressing the different query facets. We propose a two-step algorithm based on a relevance feedback process which first performs a document scoring towards each expert and then allocates documents to the most suitable experts using the Expectation-Maximisation learning-method. The performance improvement is demonstrated through experiments using TREC interactive benchmark. This paper has been awarded at AIRS 2013 Conference. http://link.springer.com/chapter/10.1007%2F978-3-642-45068-6_10 ftp://ftp.irit.fr/IRIT/SIG/2013_AIRS_STB.pdf]]>
This slides presents a novel collaborative document ranking model which aims at solving a complex information retrieval task involving a multi-faceted information need. For this purpose, we consider a group of users, viewed as experts, who collaborate by addressing the different query facets. We propose a two-step algorithm based on a relevance feedback process which first performs a document scoring towards each expert and then allocates documents to the most suitable experts using the Expectation-Maximisation learning-method. The performance improvement is demonstrated through experiments using TREC interactive benchmark. This paper has been awarded at AIRS 2013 Conference. http://link.springer.com/chapter/10.1007%2F978-3-642-45068-6_10 ftp://ftp.irit.fr/IRIT/SIG/2013_AIRS_STB.pdf]]> Wed, 18 Dec 2013 02:18:54 GMT /slideshow/a-collaborative-document-ranking-model-for-a-multifaceted-search-29313464/29313464 soulierlaure@slideshare.net(soulierlaure) A Collaborative Document Ranking Model for a Multi-Faceted Search soulierlaure This slides presents a novel collaborative document ranking model which aims at solving a complex information retrieval task involving a multi-faceted information need. For this purpose, we consider a group of users, viewed as experts, who collaborate by addressing the different query facets. We propose a two-step algorithm based on a relevance feedback process which first performs a document scoring towards each expert and then allocates documents to the most suitable experts using the Expectation-Maximisation learning-method. The performance improvement is demonstrated through experiments using TREC interactive benchmark. This paper has been awarded at AIRS 2013 Conference. http://link.springer.com/chapter/10.1007%2F978-3-642-45068-6_10 ftp://ftp.irit.fr/IRIT/SIG/2013_AIRS_STB.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/airsv0-131218021854-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This slides presents a novel collaborative document ranking model which aims at solving a complex information retrieval task involving a multi-faceted information need. For this purpose, we consider a group of users, viewed as experts, who collaborate by addressing the different query facets. We propose a two-step algorithm based on a relevance feedback process which first performs a document scoring towards each expert and then allocates documents to the most suitable experts using the Expectation-Maximisation learning-method. The performance improvement is demonstrated through experiments using TREC interactive benchmark. This paper has been awarded at AIRS 2013 Conference. http://link.springer.com/chapter/10.1007%2F978-3-642-45068-6_10 ftp://ftp.irit.fr/IRIT/SIG/2013_AIRS_STB.pdf

A Collaborative Document Ranking Model for a Multi-Faceted Search from UPMC - Sorbonne Universities

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