Building user interest profiles from wikipedia clusters

Users of search systems are often reluctant to explicitly build profiles to indicate their search interests. Thus automatically building user profiles is an important research area for personalized search. One difficult component of doing this is accessing a knowledge system which provides broad coverage of user search interests. In this work, we describe a method to build category id based user profiles from a user's historical search data. Our approach makes significant use of Wikipedia as an external knowledge resource

Bespoke Image Search Engine Based On User Sensitivity

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Ontology Based Personalized Search Engine

An ontology is a representation of knowledge as hierarchies of concepts within domain, using a shared vocabulary to denote the types, properties and inter-relationships of those concepts [1][2]. Ontologies are often equated with classification of hierarchies of classes, class definitions, and the relations, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions, i.e., in the traditional logic sense that only introduce terminology and do not add any knowledge about the world (Enderton, 1972). To specify a conceptualization, axioms need to be proposed that constrain interpretation of defined terms [3]. Ontologies are frameworks for organizing information and are collections of URIs. It is a systematic arrangement of all important categories of objects and concepts within a particular field and relationship between them. Search engines are commonly used for information retrieval from web. The ontology based personalized search engine (OPSE) captures the user’s priorities in the form of concepts by mining through the data which has been previously clicked by them. Search results need to be provided according to user profile and user interest so that highly relevant search data is provided to the user. In order to do this, user profiles need to be maintained. Location information is important for searching data; OPSE needs to classify concepts into content concepts and location concepts. User locations (gathered during user registration) are used to supplement the location concepts in OPSE. Ontology based user profiles are used to organize user preferences and adapt personalized ranking function in order for relevant documents to be retrieved according to a suitable ranking. A client-server architecture is used for design of ontology based personalized search engine. The design involves in collecting and storing client clickthrough data. Functionalities such as re-ranking and concept extraction can be performed at the server side of personalized search engine. As an additional requirement, we can address the privacy issue by restricting the information in the user profile exposed to the personalized mobile search engine server with some privacy parameters. The Prototype of OPSE will be developed on the web platform. Ontology based personalized search engines can significantly improve the precision of results

Ontology-Based Recommendation of Editorial Products

Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Proposition des cadres d'évaluation d'un système de RI personnalisé

National audienceL'évaluation d'un système de recherche d'information (RI) personnalisé consiste principalement à mesurer ses performances. Les cadres d'évaluation classiques en RI basés sur les approches orientées laboratoire méritent d'être étendues et révisées vu que le contexte de recherche de l'utilisateur n'est pas considéré dans le protocole d'évaluation et les collections de test. Nous présentons dans ce papier des cadres d'évaluation adaptés à un système de RI personnalisé basés sur l'enrichissement des collections TREC par des contextes/profils utilisateur simulés. Plus précisément, un protocole issu de TREC adhoc consiste à construire des profils utilisateur à partir des domaines d'intérêts prédéfinis dans TREC adhoc. Le protocole issu de TREC HARD consiste à construire le profil à partir des sessions de recherche simulées par les sujets des requêtes de la collection. Les résultats obtenus confirment la stabilité de la performance de notre modèle de RI personnalisé selon les cadres proposés sur des collections de test différentes

Agente inteligente para la identificación automática de perfiles de usuarios de turismo

El dominio de turismo es un ambiente muy dinámico, los productos (paquetes turísticos) cambian continuamente, como así también los intereses de los clientes (turistas). Por ejemplo, las preferencias sobre un tipo de lectura son más estables y constantes que las preferencias sobre las visitas turísticas. Las diferentes fuentes de información para poder predecir preferencias más completas, como así también la consideración de múltiples variables para poder realizar las recomendaciones, son los temas centrales a los que apunta el proyecto. En este aspecto el proyecto permitirá el desarrollo e implementación de innovadoras tecnologías de software aplicables al turismo regional que ayudará a mejorar la efectividad de los sitios web locales en cuanto a la atracción de turistas. La incorporación de diferentes fuentes de información del turista y los métodos y técnicas para su recuperación incrementaría la precisión de las recomendaciones. Recomendaciones más precisas incrementa la satisfacción del turista, ocasionando la fidelidad del turista e incrementando la popularidad del sitio web permitiendo una más amplia difusión del turismo local y promoción del patrimonio local. Como así también permitirá definir paquetes turísticos en función de la caracterización del perfil dinámico del turista que se obtendrá a partir de la interacción del turista con los sitios web.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Inferring semantic relations by user feedback

In the last ten years, ontology-based recommender systems have been shown to be effective tools for predicting user preferences and suggesting items. There are however some issues associated with the ontologies adopted by these approaches, such as: 1) their crafting is not a cheap process, being time consuming and calling for specialist expertise; 2) they may not represent accurately the viewpoint of the targeted user community; 3) they tend to provide rather static models, which fail to keep track of evolving user perspectives. To address these issues, we propose Klink UM, an approach for extracting emergent semantics from user feedbacks, with the aim of tailoring the ontology to the users and improving the recommendations accuracy. Klink UM uses statistical and machine learning techniques for finding hierarchical and similarity relationships between keywords associated with rated items and can be used for: 1) building a conceptual taxonomy from scratch, 2) enriching and correcting an existing ontology, 3) providing a numerical estimate of the intensity of semantic relationships according to the users. The evaluation shows that Klink UM performs well with respect to handcrafted ontologies and can significantly increase the accuracy of suggestions in content-based recommender systems