Yongli Ren

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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)
— 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)
In this paper, we tackle the incompleteness of user rating history in the context of collaborative filtering for Top-N recommendations. Previous research ignore a fact that two rating patterns exist in the user × item rating matrix and influence each other. More importantly, their interactive influence characterizes the development of each other, which can(More)
We report a preliminary study of mobile Web behaviour in a large indoor retail space. By analysing a Web log collected over a 1 year period at an inner city shopping mall in Sydney, Australia, we found that 1) around 60% of registered Wi-Fi users actively browse the Internet, and the rest 40% do not, with around 10% of these users using Web search engines.(More)