An Efficient and Scalable Recommender System for the Smart Web

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dc.contributor.author Baldominos Gómez, Alejandro
dc.contributor.author Sáez Achaerandio, Yago
dc.contributor.author Albacete García, Esperanza
dc.contributor.author Marrero, Ignacio
dc.date.accessioned 2016-02-17T11:56:53Z
dc.date.available 2016-02-17T11:56:53Z
dc.date.issued 2015-11-01
dc.identifier.bibliographicCitation Proceedings of the 2015. 11th International Conference on Innovations in Information Technology (IIT). Innovations 2015. Special Theme: Smart Cities, Big Data, Sustainable Development, pp. 296-301.
dc.identifier.isbn 978-1-4673-8509-1
dc.identifier.uri http://hdl.handle.net/10016/22318
dc.description This proceeding at: 11th International Conference on Innovations in Information Technology (IIT) Innovations 2015. Special Theme: Smart Cities, Big Data, Sustainable Development. Took place at 2015, November, 01 - 03, in Dubai, United Arab Emirates (IEEE IIT 2015).
dc.description.abstract This work describes the development of a web recommender system implementing both collaborative filtering and content-based filtering. Moreover, it supports two different working modes, either sponsored or related, depending on whether websites are to be recommended based on a list of ongoing ad campaigns or in the user preferences. Novel recommendation algorithms are proposed and implemented, which fully rely on set operations such as union and intersection in order to compute the set of recommendations to be provided to end users. The recommender system is deployed over a real-time big data architecture designed to work with Apache Hadoop ecosystem, thus supporting horizontal scalability, and is able to provide recommendations as a service by means of a RESTful API. The performance of the recommender is measured, resulting in the system being able to provide dozens of recommendations in few milliseconds in a single-node cluster setup.
dc.description.sponsorship This research work is part of Memento Data Analysis project, co-funded by the Spanish Ministry of Industry, Energy and Tourism with no. TSI-020601-2012-99 and TSI-020110-2009-137.
dc.format.extent 6
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE. Computer Society
dc.relation.ispartofseries Innovations in Information Technology/2015
dc.rights © 2015, IEEE
dc.subject.other Algorithm design and analysis
dc.subject.other Big data
dc.subject.other Collaboration
dc.subject.other Computer architecture
dc.subject.other Real-time systems
dc.subject.other Recommender systems
dc.subject.other Amart Cities
dc.subject.other Uniform resource locators.
dc.title An Efficient and Scalable Recommender System for the Smart Web
dc.type bookPart
dc.type conferenceObject
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1109/INNOVATIONS.2015.7381557
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TSI-020110-2009-137
dc.relation.projectID Gobierno de España. TSI-020601-2012-99
dc.type.version acceptedVersion
dc.relation.eventdate 2015 November, 01-03
dc.relation.eventnumber 11
dc.relation.eventplace Dubai (EEAU)
dc.relation.eventtitle International Conference on Innovations in Information Technology (IIT) Innovations 2015. (IEEE IIT 2015).
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 296
dc.identifier.publicationlastpage 301
dc.identifier.publicationtitle Proceedings of the 2015 11th International Conference on Innovations in Information Technology (IIT). Innovations 2015. Special Theme: Smart Cities, Big Data, Sustainable Development.
dc.identifier.publicationvolume 2015
dc.identifier.uxxi CC/0000024136
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