3,255 research outputs found

    Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments

    Get PDF
    In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system

    User interface patterns in recommendation-empowered content intensive multimedia applications

    Get PDF
    Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of “good” design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated from a research perspective. Still, they have been seldom explored in the arena of Recommender Systems (RSs). RSs provide suggestions (“recommendations”) for items that are likely to be appropriate for the user profile, and are increasingly adopted in content-intensive multimedia applications to complement traditional forms of search in large information spaces. This paper explores RSs through the lens of User Interface (UI) Design Patterns. We have performed a systematic analysis of 54 recommendation-empowered content-intensive multimedia applications, in order to: (i) discover the occurrences of existing domain independent UI patterns; (ii) identify frequently adopted UI solutions that are not modelled by existing patterns, and define a set of new UI patterns, some of which are specific of the interfaces for recommendation features while others can be useful also in a broader context. The results of our inspection have been discussed with and evaluated by a team of experts, leading to a consolidated set of 14 new patterns that are reported in the paper. Reusing pattern-based design solutions instead of building new solutions from scratch enables novice and expert designers to build good UIs for Recommendation-empowered content intensive multimedia applications more effectively, and ultimately can improve the UX experience in this class of systems. From a broader perspective, our work can stimulate future research bridging Recommender Systems, Web Engineering and Interface Design by means of Design Patterns, and highlights new research directions also discussed in the paper

    Graph ranking-based recommender systems

    Full text link
    University of Technology Sydney. Faculty of Engineering and Information Technology.The rapid growth of web technologies and the volume of Internet users provide excellent opportunities for large-scale online applications but also have caused increasing information overloading problems whereby users find it hard to locate relevant information to exactly meet their needs efficiently by basic Internet searching functions. Recommender systems are emerging to aim to handle this issue and provide personalized suggestions of resources (items) to particular users, which have been implemented in many domains such as online shopping assistants, information retrieval tools and decision support tools. In the current era of information explosion, recommender systems are facing some new challenges. Firstly, there are increasing tree-structured taxonomy attributes as well as freeform folksonomy tags associated with items. Secondly, there are increasing explicit and implicit social relations or correlations available for web users. Thirdly, there is increasingly diverse contextual information that affects or reflects user preferences. Furthermore, the recommendation demands of users are becoming diverse and flexible. In other words, users may have changing multi-objective recommendation requests at different times. This research aims to handle these four challenges and propose a set of recommendation approaches for different scenarios. Graph ranking theories are employed due to their ease of modelling different information entities and complex relations and their good extensibility. In different scenarios, different graphs are generated and some unique graph ranking problems are raised. Concretely, we first propose a bipartite graph random walk model for a hybrid recommender system integrating complex item content information of both tree-structured taxonomy attributes and free-form folksonomy tags. Secondly, we propose a multigraph ranking model for a multi-relational social network-based recommendation system that is able to incorporate multiple types of social relations or correlations between users. Thirdly, we propose a multipartite hypergraph ranking model for a generic full information-based recommender system that is able to handle various parities of information entities and their high-order relations. In addition, we extend the multipartite hypergraph ranking model to be able to respond to users' multi-objective recommendation requests and propose a novel multi-objective recommendation framework. We conduct comprehensive empirical experiments with a set of real-word public datasets in different domains such as movies (Movielens), music (Last.fm), e-Commerce products (Epinions) and local business (Yelp) to test the proposed graph ranking-based recommender systems. The results demonstrate that our models can generally achieve significant improvement compared to existing approaches in terms of recommendation success rate and accuracy. By these empirical experiments, we can conclude that the proposed graph ranking models are able to handle well the indicated four key challenges of recommender systems in the current era. This work is hence of both theoretical and practical significances in the field of both graph ranking and recommender systems

    A Fuzzy Tree Matching-Based Personalized E-Learning Recommender System

    Full text link
    © 1993-2012 IEEE. The rapid development of e-learning systems provides learners with great opportunities to access learning activities online, and this greatly supports and enhances the learning practices. However, an issue reduces the success of application of e-learning systems; too many learning activities (such as various leaning materials, subjects, and learning resources) are emerging in an e-learning system, making it difficult for individual learners to select proper activities for their particular situations/requirements because there is no personalized service function. Recommender systems, which aim to provide personalized recommendations for products or services, can be used to solve this issue. However, e-learning systems need to be able to handle certain special requirements: 1) leaning activities and learners' profiles often present tree structures; 2) learning activities contain vague and uncertain data, such as the uncertain categories that the learning activities belong to; 3) there are pedagogical issues, such as the precedence relations between learning activities. To deal with the three requirements, this study first proposes a fuzzy tree-structured learning activity model, and a learner profile model to comprehensively describe the complex learning activities and learner profiles. In the two models, fuzzy category trees and related similarity measures are presented to infer the semantic relations between learning activities or learner requirements. Since it is impossible to have two completely same trees, in practice, a fuzzy tree matching method is carefully discussed. A fuzzy tree matching-based hybrid learning activity recommendation approach is then developed. This approach takes advantage of both the knowledge-based and collaborative filtering-based recommendation approaches, and considers both the semantic and collaborative filtering similarities between learners. Finally, an e-learning recommender system prototype is well designed and developed based on the proposed models and recommendation approach. Experiments are done to evaluate the proposed recommendation approach, and the experimental results demonstrate the good accuracy performance of the proposed approach. A comprehensive case study about learning activity recommendation further demonstrates the effectiveness of the fuzzy tree matching-based personalized e-learning recommender system in practice

    Knowledge aggregation in people recommender systems : matching skills to tasks

    Get PDF
    People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessària. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives.Postprint (published version
    • …
    corecore