Titlebook: User Modeling, Adaptation and Personalization; 22nd International C Vania Dimitrova,Tsvi Kuflik,Geert-Jan Houben Conference proceedings 201

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Predicting User Locations and Trajectoriesto a large extent routine behavior and visits to already visited locations. In this paper, we show how daily and weekly routines can be modeled with basic prediction techniques. We compare the methods based on their performance, entropy and correlation measures. Further, we discuss how location pred

IRK

A Two-Stage Item Recommendation Method Using Probabilistic Ranking with Reconstructed Tensor Modelns. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based

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Time-Sensitive User Profile for Optimizing Search Personlizationeds and interests. To achieve this goal, many personalized search approaches explore user’s social Web interactions to extract his preferences and interests, and use them to model his profile. In our approach, the user profile is implicitly represented as a vector of weighted terms which correspond

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expdient

Hoeffding-CF: Neighbourhood-Based Recommendations on Reliably Similar Usersown that decisions made on a naive computation of user similarity are unreliable, because the number of co-ratings varies strongly among users. In this paper, we formalize the notion of . between two users and propose a method that constructs a user’s neighbourhood by selecting only those users that

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Adaptive Support versus Alternating Worked Examples and Tutored Problems: Which Leads to Better Learound that learning from examples results in faster learning in comparison to tutored problem solving in Intelligent Tutoring Systems. We present a study that compares a fixed sequence of alternating worked examples and tutored problem solving with a strategy that adaptively decides how much assistan

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