Search results for "Recommender system"
showing 10 items of 70 documents
Tracking the Preferences of Users Using Weak Estimators
2011
Published version of am article from the book:AI 2011: Advances in Artificial Intelligence. Also available from the publisher on SpringerLink:http://dx.doi.org/10.1007/978-3-642-25832-9_81 Since a social network, by definition, is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary, estimating a user’s interests, typically, involves non-stationary distributions. The consequent time varying nature of the distribution to be trac…
An intelligent architecture for service provisioning in pervasive environments
2011
Accepted version of an article from the conference: 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA). Definitive published version available from IEEE: http://dx.doi.org/10.1109/INISTA.2011.5946134 The vision of pervasive environments is being realized more than ever with the proliferation of services and computing resources located in our surrounding environments. Identifying those services that deserve the attention of the user is becoming an increasingly-challenging task. In this paper, we present an adaptive multi-criteria decision making mechanism for recommending relevant services to the mobile user. In this context Relevance is determined b…
Similarity Mashups for Recommendation
2013
Recommendation systems are becoming a state of the art for web-based systems as they produce additional product exposure and customer satisfaction. The Semantic Web and mashups can improve recommendation systems and provide new ways for their creation. In the web it is possible to analyze product descriptions and to use Linked Data for characterizing the similarity of objects or of objects and user interests. In this chapter, we give a brief overview of existing technical approaches and tools for creating recommendation systems that can be used to create mashups as recommendation systems.
Cross-Social Network Collaborative Recommendation
2015
Online social networks have become an essential part of our daily life, and an increasing number of users are using multiple online social networks simultaneously. We hypothesize that the integration of data from multiple social networks could boost the performance of recommender systems. In our study, we perform cross-social network collaborative recommendation and show that fusing multi-source data enables us to achieve higher recommendation performance as compared to various single-source baselines.
AN ONTOLOGY-BASED RECOMMENDER SYSTEM USING HIERARCHICAL MULTICLASSIFICATION FOR ECONOMICAL E-NEWS
2014
International audience; This paper focuses on a recommender system of economic news articles. Its objectives are threefold: (i) automatically multi-classify new economic articles, (ii) recommend articles by comparing profiles of users and multi-classification of articles, and (iii) managing the vocabulary of the economic news domain to improve the system based on seamlessly intervention of documentalists. In this paper we focus on the automatic multi-classification of the articles, managed by inference process of ontologies, and the enrichment of the documentalist-oriented ontology which provides the necessary capabilities to the DL reasoner for automatic multi-classification.
PROFILE REFINEMENT IN ONTOLOGY-BASED RECOMMANDER SYSTEMS FOR ECONOMICAL E-NEWS
2014
International audience; This paper is interested in a recommender system of economic news articles. More precisely, it focuses on automatic profile refinement of customers which is an important task over time by taken into account logs of the user concerning especially his/her actions, reading time, and domain specific knowledge. In our approach, ontologies are used to describe and automatically refine these profiles. This work focuses on one particular type of recommender systems which is content-based recommenders. The aim of these recommender systems is to build a user profile and to improve its precision over time. Several improvements that have been made to these recommender systems ov…
Information indexing and recommendation : toward a precise description if items by an ontological approach based on business domain modeling : applic…
2015
Effective management of large amounts of information has become a challenge increasinglyimportant for information systems. Everyday, new information sources emerge on the web. Someonecan easily find what he wants if (s)he seeks an article, a video or a specific artist. However,it becomes quite difficult, even impossible, to have an exploratory approach to discover newcontent. Recommender systems are software tools that aim to assist humans to deal withinformation overload. The work presented in this Phd thesis proposes an architecture for efficientrecommendation of news. In this document, we propose an architecture for efficient recommendationof news articles. Our ontological approach relie…
AN ONTOLOGY-BASED APPROACH TO PROVIDE PERSONNALIZED RECOMMENDATIONS USING A STOCHASTIC ALGORITHM
2011
International audience; The use of personalized recommender systems to assist users in the selection of products is becoming more and more popular and wide-spread. The purpose of a recommender system is to provide the most suitable items from an knowledge base, according the user knowledge, tastes, interests, ... These items are generally proposed as ordered lists. In this article, we propose to combine works from adaptive hypermedia systems, semantic web and combinatory to create a new kind of recommender systems suggesting combinations of items corresponding to the user.
A Semantic-based Recommender System Using A Simulated Annealing Algorithm
2010
International audience; A recommender system based on semantic web technologies and on an adaptive hypermedia architecture is shown in this paper. The system uses a stochastic algorithm to provide recommendations to users. The paper presents the system architecture based on the semantic Web technologies and explains a simulated annealing algorithm performing the recommendations. A mobile application for the tourism domain proving the feasibility of this system is described at the end of the paper, some benchmarks are presented. In this application, the recommendations are defined as combinations of tourism products, which are linked to each other. The paper is mainly focused on the architec…
Semantically-enhanced advertisement recommender systems in social networks
2017
El objetivo principal de la investigación es estudiar y diseñar un entorno de recomendación publicitaria en las redes sociales que puede ser enriquecido mediante tecnologías semánticas. A pesar de que existen muchas aplicaciones y soluciones para los sistemas de recomendación, en este estudio se diseña un framework robusto con un rendimiento adecuado para poder ser implementado en las redes sociales con el objetivo de ampliar los propósitos de negocio. De este objetivo principal se pueden derivar los siguientes objetivos secundarios: 1. Superar las limitaciones iniciales de los métodos clásicos de recomendación. 2. Aumentar la calidad y precisión de las recomendaciones y el rendimiento del …