Towards persuasive social recommendation: knowledge model
| Autor: | Palanca Cámara, Javier, Heras Barberá, Stella María, Jorge Cano, Javier, Julian Inglada, Vicente Javier |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Process (engineering)
Computer science business.industry Fingerprint (computing) Social environment Ocean Engineering Recommender system computer.software_genre Social networks World Wide Web Recommender systems Graph (abstract data type) The Internet Data integration business computer LENGUAJES Y SISTEMAS INFORMATICOS |
| Zdroj: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| Popis: | [EN] The exponential growth of social networks makes fingerprint let by users on the Internet a great source of information, with data about their preferences, needs, goals, profile and social environment. These data are distributed across di↵erent sources of information (social networks, blogs, databases, etc.) that may contain inconsistencies and their accuracy is uncertain. Paradoxically, this unprecedented availability of heterogeneous data has meant that users have more information available than they actually are able to process and understand to extract useful knowledge from it. Therefore, new tools that help users in their decision-making processes within the network (e.g. which friends to contact with or which products to consume) are needed. In this paper, we show how we have used a graph-based model to extract and model data and transform it in valuable knowledge to develop a persuasive social recommendation system1. This work was partially supported by the project MINE-CO/FEDER TIN2012-365686-C03-01 of the Spanish government and by the Spanish Ministry of Education, Culture and Sports under the Program for R&D Valorisation and Joint Resources VLC/CAMPUS, as part of the Campus of International Excellence Program (Ref. SP20140788). |
| Databáze: | OpenAIRE |
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