Collaborative Information Retrieval: Concepts, Models and Evaluation

International audienceRecent work have shown the potential of collaboration for solving complex or exploratory search tasks allowing to achieve synergic effects with respect to individual search, which is the prevalent information retrieval (IR) setting this last decade. This interactive multiuser context gives rise to several challenges in IR. One main challenge relies on the adaptation of IR techniques or models [8] in order to build algo-rithmic supports of collaboration distributing documents among users. The second challenge is related to the design of Collaborative Information Retrieval (CIR) models and their effectiveness evaluation since individual IR frameworks and measures do not totally fit with the collaboration paradigms. In this tutorial, we address the second challenge and present first a general overview of collaborative search introducing the main underlying notions. Then, we focus on related work dealing with collaborative ranking models and their effectiveness evaluation. Our primary objective is to introduce these notions by highlighting how and why they should be different from individual IR in order to give participants the main clues for investigating new research directions in this domain with a deep understanding of current CIR work

Biomedical concept extraction based on combining the content-based and word order similarities

International audienceIt is well known that the main objective of conceptual retrieval models is to go beyond simple term matching by relaxing term independence assumption through concept recognition. In this paper, we present an approach of semantic indexing and retrieval of biomedical documents through the process of identifying domain concepts extracted from the Medical Subject Headings (MeSH) thesaurus. Our indexing approach relies on a purely statistical vector space model, which represents medical documents and MeSH concepts as term vectors. By leveraging a combination of the bag-of-word concept representation and word positions in the textual features, we demonstrate that our mapping method is able to extract valuable concepts from documents. The output of this semantic mapping serves as the input to our relevance document scoring in response to a query. Experiments on the OHSUMED collection show that our semantic indexing method significantly outperforms state-of-art baselines that employ word or term statistics

Définition d'un profil multidimensionnel de l'utilisateur : Vers une technique basée sur l'interaction entre dimensions

National audienceLa personnalisation d'un processus d'accès à l'information a pour objectif de délivrer à l'utilisateur une information appropriée à ses préférences, ses centres d'intérêts ou plus globalement son profil. Ce papier présente une technique de construction du profil de l'utilisateur qui s'inscrit dans une approche statistique utilisant le comportement de l'utilisateur comme source permettant de prédire implicitement son modèle. Cette technique s'articule plus particulièrement sur l'interaction entre dimensions du profil représentées par l'historique des recherches et centres d'intérêt de l'utilisateur

Définition et exploitation des méta-rôles des utilisateurs pour la recherche d'information collaborative

National audienceLa recherche d'information collaborative est un processus particulier impliquant un ensemble d'utilisateurs partageant un même besoin en information. Dans ce contexte, l'exploi-tation de la division du travail au travers des rôles est une des techniques utilisées pour structu-rer la session de recherche et optimiser son efficacité. Dans ce papier, nous proposons d'étudier les caractéristiques de comportement d'une paire de collaborateurs sur la base d'hypothèses de leur complémentarité. Nous définissons ainsi la notion de rôles latents qui sont (a) détectés en temps réel et (b) ensuite réinjectés dans un modèle d'ordonnancement de documents. Les expérimentations, menées sur des fichiers logs de sessions de collaboration réelles impliquant des paires d'utilisateurs, mettent en évidence l'efficacité de notre approche comparativement à des stratégies de recherche individuelles ou à celles qui considèrent des rôles fixes. ABSTRACT. Collaborative information retrieval is a particular setting involving a set of users sharing the same information need. In this context, the application of the division of labor policy through collaborators' roles is generally used in order to structure the search session and enhance its retrieval effectiveness. In this paper, we propose to analyse the search features of pairwise collaborators allowing to identify their implicit roles according to research hypothesis based on their complementarity. We define the notion of collaborators' meta-role which is (a) identified in real time, and (b) reinjected within a collaborative document ranking model. The experimental evaluation performed on search logs of real collaborative search session involving pairs of users highlights the effectiveness of our model with respect to individual-based or fixed roles-based search sessions. MOTS-CLÉS : recherche d'information collaborative, rôles, modèle d'ordonnancement, complé-mentarité des comportement

Applying Heuristics to Improve A Genetic Query Optimisation Process in Information Retrieval

International audienceThis work presents a genetic approach for query optimisation in information retrieval. The proposed GA is improved y heuristics in order to solve the relevance multimodality problem and adapt the genetic exploration process to the information retrieval task. Experiments with AP documents and queries issued from TREC show the effectiveness of our GA mode

Understanding the Impact of the Role Factor in Collaborative Information Retrieval

International audienceCollaborative 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: Frameworks, Theoretical Models, and Emerging Topics

International audienceA 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

QUERY OPTIMISATION USING AN IMPROVED GENETIC ALGORITHM

International audienceThis paper presents an approach to intelligent information retrieval based on genetic heuristics. Recent search has shown that applying genetic models for query optimisation improve the retrieval effectiveness. We investigate ways to improve this process by combining genetic heuristics and information retrieval techniques. More precisely, we propose to integrate relevance feedback techniques to perform the genetic operators and the speciation heuristic to solve the relevance multimodality problem. Experiments, with AP documents and queries issued from TREC, showed the effectiveness of our approach. Keywords: Informatio

Un Algorithme génétique spécifique à une reformulation multi-requêtes dans un système de recherche d'information

National audienceCet article présente une approche de reformulation de requête fondée sur l'utilisation combinée de la stratégie d'injection de pertinence et des techniques avancées de l'algorithmique génétique. Nous proposons un processus génétique d'optimisation multi-requêtes amélioré par l'intégration des heuristiques de nichage et adaptation des opérateurs génétiques. L'heuristique de nichage assure une recherche d'information coopérative dans différentes directions de l'espace documentaire. L'intégration de la connaissance à la structure des opérateurs permet d'améliorer les conditions de convergence de l'algorithme. Nous montrons, à l'aide d'expérimentations réalisées sur une collection TREC, l'intérêt de notre approche