User Modeling, Adaptation, and Personalization: 21th by à tefan Pero, Tomáš Horváth (auth.), Sandra Carberry,

By à tefan Pero, Tomáš Horváth (auth.), Sandra Carberry, Stephan Weibelzahl, Alessandro Micarelli, Giovanni Semeraro (eds.)

This booklet constitutes the completely refereed lawsuits 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 quite a few submissions. The papers conceal the next subject matters: recommender platforms, pupil modeling, social media and groups, human cognition, character, privateness, internet curation and person profiles, trip and cellular purposes, and platforms for aged and disabled individuals.

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User modelling techniques have been applied to these mechanisms, resulting in personalised notifications and news feeds to support users in finding interesting content [2, 6, 16]. Popular approaches for recommendations in SNs leverage the underlying social graph to identify people and content and have seen great success in friendship based networks [4, 7]. , SNs for health or the environment. There, implied networks (also refer to as implicit networks) built through interactions are more commonplace.

Our analysis of ranking methods indicated that the use of a community trust is useful with a social graph algorithm, but this parameter does not impact on the performance of social behaviour based algorithms. We will investigate this further in future work. References 1. : Measuring behavioral trust in social networks. In: 2010 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 150–152 (2010) 2. : Personalized network updates: increasing social interactions and contributions in social networks.

In general, for a target member, the recommender systtem identifies and ranks a set of o content items that members in their network have innteracted with, as shown in Fiig, 1 (a). Several variables contribute to the content iteems relevance score: (1) the netw work, (2) the member-to-member trust, (3) the memberr-tocontent trust and (4) the member-to-community m trust. We now describe the optiions considered for (1-4). 1 Implicit and Expliccit Networks Traditional SN representattions reflect articulated friendships, binary in nature and containing linkages only beetween existing “friends” (network containing explicit trrust only in Fig.

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