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

advocate

書目名稱User Modeling, Adaptation and Personalization影響因子(影響力)




書目名稱User Modeling, Adaptation and Personalization影響因子(影響力)學(xué)科排名




書目名稱User Modeling, Adaptation and Personalization網(wǎng)絡(luò)公開度




書目名稱User Modeling, Adaptation and Personalization網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱User Modeling, Adaptation and Personalization被引頻次




書目名稱User Modeling, Adaptation and Personalization被引頻次學(xué)科排名




書目名稱User Modeling, Adaptation and Personalization年度引用




書目名稱User Modeling, Adaptation and Personalization年度引用學(xué)科排名




書目名稱User Modeling, Adaptation and Personalization讀者反饋




書目名稱User Modeling, Adaptation and Personalization讀者反饋學(xué)科排名

騙子

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

以煙熏消毒

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.

開始沒有

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

縫紉

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.

控訴

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.

不要不誠實

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%.