User-Centered Social Information Retrieval Model Exploiting Annotations and Social Relationships

International audienceSocial Information Retrieval (SIR) has extended the classical information retrieval models and systems to take into account social information of the user within his social networks. We assume that a SIR system can exploit the informational social context (ISC) of the user in order to refine his retrieval, since different users may express different information needs as the same query. Hence, we present a SIR model that takes into account the user's social data, such as his annotations and his social relationships through social networks. We propose to integrate the user's ISC into the documents indexing process, allowing the SIR system to personalize the list of documents returned to the user. Our approach has shown interesting results on a test collection built from the social collaborative bookmarking network Delicious

Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure

Share and reuse of context metadata resulting from interactions between users and heterogeneous web-based learning environments

L'intérêt pour l'observation, l'instrumentation et l'évaluation des systèmes éducatifs en ligne est devenu de plus en plus important ces dernières années au sein de la communauté des Environnements Informatique pour l'Apprentissage Humain (EIAH). La conception et le développement d'environnements d'apprentissage en ligne adaptatifs (AdWLE - Adaptive Web-based Learning Environments) représentent une préoccupation majeure aujourd'hui, et visent divers objectifs tels que l'aide au processus de réingénierie, la compréhension du comportement des utilisateurs, ou le soutient à la création de systèmes tutoriels intelligents. Ces systèmes gèrent leur processus d'adaptation sur la base d'informations détaillées reflétant le contexte dans lequel les étudiants évoluent pendant l'apprentissage : les ressour-ces consultées, les clics de souris, les messages postés dans les logiciels de messagerie instantanée ou les forums de discussion, les réponses aux questionnaires, etc. Les travaux présentés dans ce document sont destinés à surmonter certaines lacunes des systèmes actuels en fournissant un cadre dédié à la collecte, au partage et à la réutilisation du contexte représenté selon deux niveaux d'abstraction : le contexte brut (résultant des interactions directes entre utilisateurs et applications) et le contexte inféré (calculé à partir des données du contexte brut). Ce cadre de travail qui respecte la vie privée des usagers est fondé sur un standard ouvert dédié à la gestion des systèmes, réseaux et applications. Le contexte spécifique aux outils hétérogènes constituant les EIAHs est représenté par une structure unifiée et extensible, et stocké dans un référentiel central. Pour faciliter l'accès à ce référentiel, nous avons introduit une couche intermédiaire composée d'un ensemble d'outils. Certains d'entre eux permettent aux utilisateurs et applications de définir, collecter, partager et rechercher les données de contexte qui les intéressent, tandis que d'autres sont dédiés à la conception, au calcul et à la délivrance des données de contexte inférées. Pour valider notre approche, une mise en œuvre du cadre de travail proposé intègre des données contextuelles issues de trois systèmes différents : deux plates-formes d'apprentissage Moodle (celle de l'Université Paul Sabatier de Toulouse, et une autre déployée dans le cadre du projet CONTINT financé par l'Agence Nationale de la Recherche) et une instanciation locale du moteur de recherche de la fondation Ariadne. A partir des contextes collectés, des indicateurs pertinents ont été calculés pour chacun de ces environnements. En outre, deux applications qui exploitent cet ensemble de données ont été développées : un système de recommandation personnalisé d'objets pédagogiques ainsi qu'une application de visualisation fondée sur les technologies tactiles pour faciliter la navigation au sein de ces données de contexte.An interest for the observation, instrumentation, and evaluation of online educational systems has become more and more important within the Technology Enhanced Learning community in the last few years. Conception and development of Adaptive Web-based Learning Environments (AdWLE) in order to facilitate the process of re-engineering, to help understand users' behavior, or to support the creation of Intelligent Tutoring Systems represent a major concern today. These systems handle their adaptation process on the basis of detailed information reflecting the context in which students evolve while learning: consulted resources, mouse clicks, chat messages, forum discussions, visited URLs, quizzes selections, and so on. The works presented in this document are intended to overcome some issues of the actual systems by providing a privacy-enabled framework dedicated to the collect, share and reuse of context represented at two abstraction levels: raw context (resulting from direct interactions between users and applications) and inferred context (calculated on the basis of raw context). The framework is based on an open standard dedicated to system, network and application management, where the context specific to heterogeneous tools is represented as a unified and extensible structure and stored into a central repository. To facilitate access to this context repository, we introduced a middleware layer composed of a set of tools. Some of them allow users and applications to define, collect, share and search for the context data they are interested in, while others are dedicated to the design, calculation and delivery of inferred context. To validate our approach, an implementation of the suggested framework manages context data provided by three systems: two Moodle servers (one running at the Paul Sabatier University of Toulouse, and the other one hosting the CONTINT project funded by the French National Research Agency) and a local instantiation of the Ariadne Finder. Based on the collected context, relevant indicators have been calculated for each one of these environments. Furthermore, two applications which reuse the encapsulated context have been developed on top of the framework: a personalized system for recommending learning objects to students, and a visualization application which uses multi-touch technologies to facilitate the navigation among collected context entities

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems

Personalisation, adaptation and recommendation are central features of TEL environments. In this context, information retrieval techniques are applied as part of TEL recommender systems to filter and recommend learning resources or peer learners according to user preferences and requirements. However, the suitability and scope of possible recommendations is fundamentally dependent on the quality and quantity of available data, for instance, metadata about TEL resources as well as users. On the other hand, throughout the last years, the Linked Data (LD) movement has succeeded to provide a vast body of well-interlinked and publicly accessible Web data. This in particular includes Linked Data of explicit or implicit educational nature. The potential of LD to facilitate TEL recommender systems research and practice is discussed in this paper. In particular, an overview of most relevant LD sources and techniques is provided, together with a discussion of their potential for the TEL domain in general and TEL recommender systems in particular. Results from highly related European projects are presented and discussed together with an analysis of prevailing challenges and preliminary solutions.LinkedU

May I Suggest? Comparing Three PLE Recommender Strategies

Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general

A two-step learning approach for solving full and almost full cold start problems in dyadic prediction

Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic prediction, a setting that occurs when both objects in an out-of-sample dyad have not been observed during training, or if one of them has been observed, but very few times. A popular approach for addressing this problem is to train a model that makes predictions based on a pairwise feature representation of the dyads, or, in case of kernel methods, based on a tensor product pairwise kernel. As an alternative to such a kernel approach, we introduce a novel two-step learning algorithm that borrows ideas from the fields of pairwise learning and spectral filtering. We show theoretically that the two-step method is very closely related to the tensor product kernel approach, and experimentally that it yields a slightly better predictive performance. Moreover, unlike existing tensor product kernel methods, the two-step method allows closed-form solutions for training and parameter selection via cross-validation estimates both in the full and almost full cold start settings, making the approach much more efficient and straightforward to implement

Adaptive Visualization for Focused Personalized Information Retrieval

The new trend on the Web has totally changed todays information access environment. The traditional information overload problem has evolved into the qualitative level beyond the quantitative growth. The mode of producing and consuming information is changing and we need a new paradigm for accessing information.Personalized search is one of the most promising answers to this problem. However, it still follows the old interaction model and representation method of classic information retrieval approaches. This limitation can harm the potential of personalized search, with which users are intended to interact with the system, learn and investigate the problem, and collaborate with the system to reach the final goal.This dissertation proposes to incorporate interactive visualization into personalized search in order to overcome the limitation. By combining the personalized search and the interac- tive visualization, we expect our approach will be able to help users to better explore the information space and locate relevant information more efficiently.We extended a well-known visualization framework called VIBE (Visual Information Browsing Environment) and implemented Adaptive VIBE, so that it can fit into the per- sonalized searching environment. We tested the effectiveness of this adaptive visualization method and investigated its strengths and weaknesses by conducting a full-scale user study.We also tried to enrich the user models with named-entities considering the possibility that the traditional keyword-based user models could harm the effectiveness of the system in the context of interactive information retrieval.The results of the user study showed that the Adaptive VIBE could improve the precision of the personalized search system and could help the users to find out more diverse set of information. The named-entity based user model integrated into Adaptive VIBE showed improvements of precision of user annotations while maintaining the level of diverse discovery of information

Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching