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In this paper, we observe that the user preference styles tend to change regularly following certain patterns. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a preference pattern is defined as a set of(More)
As a popular technique in recommender systems, Collaborative Filtering (CF) has received extensive attention in recent years. However, its privacy-related issues, especially for <i>neighborhood-based</i> CF methods, can not be overlooked. The aim of this study is to address the privacy issues in the context of <i>neighborhood-based</i> CF methods by(More)
As each user tends to rate a small proportion of available items, the resulted Data Sparsity issue brings significant challenges to the research of recommender systems. This issue becomes even more severe for neighborhood-based collaborative filtering methods, as there are even lower numbers of ratings available in the neighborhood of the query item. In(More)
Due to the significant amount of data generated by modern medicine there is a growing reliance on tools such as data mining and knowledge discovery to help make sense and comprehend such data. The success of this process requires collaboration and interaction between such methods and medical professionals. Therefore an important question is: How can we(More)
Internetware is envisioned as a new software paradigm for software development in platforms such as the Internet. The reliability of the developed software becomes a key challenge due to the open, dynamic and uncertain nature of such environment. To make the development more reliable, it is necessary to evaluate the trustworthiness of the resource providers(More)
— Rapid growth of technical developments has created huge challenges for microphone forensics-a sub-category of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more(More)
As one of the biggest challenges in research on recommender systems, the data sparsity issue is mainly caused by the fact that users tend to rate a small proportion of items from the huge number of available items. This issue becomes even more problematic for the neighborhood-based collaborative filtering (CF) methods, as there are even lower numbers of(More)