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

advocate

书目名称User Modeling, Adaptation and Personalization影响因子(影响力)




书目名称User Modeling, Adaptation and Personalization影响因子(影响力)学科排名




书目名称User Modeling, Adaptation and Personalization网络公开度




书目名称User Modeling, Adaptation and Personalization网络公开度学科排名




书目名称User Modeling, Adaptation and Personalization被引频次




书目名称User Modeling, Adaptation and Personalization被引频次学科排名




书目名称User Modeling, Adaptation and Personalization年度引用




书目名称User Modeling, Adaptation and Personalization年度引用学科排名




书目名称User Modeling, Adaptation and Personalization读者反馈




书目名称User Modeling, Adaptation and Personalization读者反馈学科排名

骗子

Hoeffding-CF: Neighbourhood-Based Recommendations on Reliably Similar Userss 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 are reliably similar to her. Our method combines a statistical test and the notion of a .. We report our results on typical benchmark datasets.

CROAK

以烟熏消毒

User Partitioning Hybrid for Tag Recommendation. The user partitioning hybrid learns a different set of weights for these user partitions. Our rigorous experimental results show a marked improvement. Moreover, analysis of the partitions within a dataset offers interesting insights into how users interact with social annotations systems.

开始没有

Using DBpedia as a Knowledge Source for Culture-Related User Modelling Questionnaires utilised as a knowledge source in an interactive user modelling system. A user study, which examines the system usability and the accuracy of the resulting user model, demonstrates the potential of using DBpedia for generating culture-related user modelling questionnaires and points at issues for further investigation.

ovation

缝纫

neurologist

Recommendation Based on Contextual Opinionsher combined with users’ context-independent preferences for performing recommendation. The empirical results on two real-life datasets demonstrate that our method is capable of capturing users’ contextual preferences and achieving better recommendation accuracy than the related works.

控诉

A Two-Stage Item Recommendation Method Using Probabilistic Ranking with Reconstructed Tensor Modelmatrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.

不要不诚实

A Computational Model for Mood Recognitionotations of the well-established HUMAINE database. Our analysis indicates that we can approximate fairly accurately the human process of summarizing the emotional content of a video in a mood estimation. A moving average function with exponential discount of the past emotions achieves mood prediction accuracy above 60%.