Show simple item record

dc.contributor.authorFernández-Tobías I
dc.contributor.authorBraunhofer M
dc.contributor.authorElahi M
dc.contributor.authorRicci F
dc.contributor.authorCantador I
dc.contributor.editor
dc.date2017-02-06T00:00:00Z
dc.date.accessioned2017-03-27T14:32:01Z
dc.date.available2017-03-27T14:32:01Z
dc.date.issued2016
dc.identifier.issn0924-1868
dc.identifier.urihttp://dx.doi.org/10.1007/s11257-016-9172-z
dc.identifier.urihttp://link.springer.com/article/10.1007/s11257-016-9172-z
dc.identifier.urihttp://hdl.handle.net/10863/1974
dc.description.abstractThe new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3–40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.en_US
dc.language.isoenen_US
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s11257-016-9172-z
dc.titleAlleviating the new user problem in collaborative filtering by exploiting personality informationen_US
dc.typeArticleen_US
dc.date.updated2017-02-11T11:22:36Z
dc.publication.title
dc.language.isiEN-GB
dc.journal.titleUser Modeling and User-Adapted Interaction
dc.description.fulltextopenen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record