59,526 research outputs found
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
A look at cloud architecture interoperability through standards
Enabling cloud infrastructures to evolve into a transparent platform while preserving integrity raises interoperability issues. How components are connected needs to be addressed. Interoperability requires standard data models and communication encoding technologies compatible with the existing Internet infrastructure. To reduce vendor lock-in situations, cloud computing must implement universal strategies regarding standards, interoperability and portability. Open standards are of critical importance and need to be embedded into interoperability solutions. Interoperability is determined at the data level as well as the service level. Corresponding modelling standards and integration solutions shall be analysed
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A decision support system for fostering smart energy efficient districts
The role of ICT is becoming prominent in tackling some of the urban societal challenges such as energy
wastage and increasing carbon emissions. In this context, the concept of DAREED aims to deliver an
integrated decision support system (DSS) to drive energy efficiency and low carbon activities at both a
building and district level. The main aim of this paper is to present the technical concept of the Best
Practices recommendation component of the DAREED system. This component seeks to compare and
identify existing best practices to recommend practical actions to various stakeholders (e.g. building
managers, citizens) in order to improve energy performance considering the global needs of a building.
This paper also discusses the context of the three field trial sites (based in UK, Spain and Italy) in which
the DAREED platform along with the best practices tool is to be tested and validated.This work evolved in the context of the project DAREED (Decision support Advisor for innovative
business models and useR engagement for smart Energy Efficient Districts), www.dareed.eu, a project cofunded
by the EC within FP7, Grant agreement no: 609082
Selection of Software Product Line Implementation Components Using Recommender Systems: An Application to Wordpress
In software products line (SPL), there may be features which can be implemented by different components, which means there are several implementations for the same feature. In this context, the selection of the best components set to implement a given configuration is a challenging task due to the high number of combinations and options which could be selected. In certain scenarios, it is possible to find information associated with the components which could help in this selection task, such as user ratings. In this paper, we introduce a component-based recommender system, called (REcommender System that suggests implementation Components from selecteD fEatures), which uses information associated with the implementation components to make recommendations in the domain of the SPL configuration. We also provide a RESDEC reference implementation that supports collaborative-based and content-based filtering algorithms to recommend (i.e., implementation components) regarding WordPress-based websites configuration. The empirical results, on a knowledge base with 680 plugins and 187 000 ratings by 116 000 users, show promising results. Concretely, this indicates that it is possible to guide the user throughout the implementation components selection with a margin of error smaller than 13% according to our evaluation.Ministerio de Economía y Competitividad RTI2018-101204-B-C22Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-RMinisterio de Economía, Industria y Competitividad MCIU-AEI TIN2017-90644-RED
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