By à tefan Pero, Tomáš Horváth (auth.), Sandra Carberry, Stephan Weibelzahl, Alessandro Micarelli, Giovanni Semeraro (eds.)
This e-book constitutes the completely refereed lawsuits of the twenty first overseas convention on consumer Modeling, Adaption, and Personalization, held in Rome, Italy, in June 2013. The 21 lengthy and seven brief papers of the study paper song have been rigorously reviewed and chosen from quite a few submissions. The papers disguise the subsequent subject matters: recommender structures, pupil modeling, social media and groups, human cognition, character, privateness, internet curation and consumer profiles, trip and cellular functions, and platforms for aged and disabled individuals.
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This e-book constitutes the completely refereed court cases of the twenty first foreign convention on person Modeling, Adaption, and Personalization, held in Rome, Italy, in June 2013. The 21 lengthy and seven brief papers of the learn paper tune have been rigorously reviewed and chosen from various submissions.
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Extra resources for User Modeling, Adaptation, and Personalization: 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013 Proceedings
At the same time, laboratory studies and real-world online experiments indicated that higher predictive accuracy does not always correspond to the higher levels of user-perceived quality or to increased sales [5,6]. In fact, content-based approaches showed to work surprisingly well in these studies and recent others such as . With respect to precision and recall – the most popular accuracy metrics according to  – recent work also showed that popularity-based methods can represent a comparably strong baseline (, ).
310–319. ACM, New York (2008) 13. : Interestmap: Harvesting social network profiles for recommendations. In: Beyond Personalization-IUI (2005) 14. : Towards a gravity-based trust model for social networking systems. In: Proceedings of the International Conference on Distributed Computing Systems Workshops, p. 24. IEEE Computer Society, Los Alamitos (2007) 15. : STrust: a trust model for Social Networks. In: IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp.
Koren-MF also belongs to this category, which indicates that the recommendation of popular items is only boosted slowly over time. Finally, User-kNN and CB-Filtering initially lead to a stronger diversiﬁcation of the ratings which then remains stable or increases again. While the eﬀects are clearly ampliﬁed through our speciﬁc simulation strategy, we believe that the obtained results indicate that there are signiﬁcant diﬀerences between algorithms, which should be taken into account when looking for an appropriate recommendation strategy.