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dc.contributor.authorBraunhofer M
dc.contributor.authorElahi M
dc.contributor.authorRicci F
dc.contributor.editor
dc.date.accessioned2018-08-08T09:48:25Z
dc.date.available2018-08-08T09:48:25Z
dc.date.issued2014
dc.identifier.issn1724-8035
dc.identifier.urihttp://dx.doi.org/10.3233/IA-140069
dc.identifier.urihttp://iospress.metapress.com/content/G71G13N054668045
dc.identifier.urihttp://hdl.handle.net/10863/5704
dc.description.abstractNovel research works in recommender systems have illustrated the benefits of exploiting contextual information, such as the time and location of a suggested place of interest, in order to better predict the user ratings and produce more relevant recommendations. But, when deploying a context-aware system one must put in place techniques for operating in the cold-start phase, i.e., when no or few ratings are available for the items listed in the system catalogue and it is therefore hard to predict the missing ratings and compose relevant recommendations. This problem has not been directly tackled in previous research. Hence, in order to address it, we have designed and implemented several novel algorithmic components and interface elements in a fully operational points of interest (POI) mobile recommender system (STS). In particular, in this article we illustrate the benefits brought by using the user personality and active learning techniques. We have developed two extended versions of the matrix factorisation algorithm to identify what items the users could and should rate and to compose personalised recommendations. While context-aware recommender systems have been mostly evaluated offline, a testing scenario that suffers from many limitations, in our analysis we evaluate the proposed system in live user studies where the graphical user interface and the full interaction design play a major role. We have measured the system effectiveness in terms of several metrics such as: the quality and quantity of acquired ratings-in-context, the recommendation accuracy (MAE), the system precision, the perceived recommendation quality, the user choice satisfaction, and the system usability. The obtained results confirm that the proposed techniques can effectively overcome the identified cold-start problem.en_US
dc.language.isoenen_US
dc.rights
dc.titleTechniques for cold-starting context-aware mobile recommender systems for tourismen_US
dc.typeArticleen_US
dc.date.updated2018-08-08T09:30:53Z
dc.publication.title
dc.language.isiEN-GB
dc.journal.titleIntelligenza Artificiale
dc.description.fulltextopenen_US


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