Yongkoo Han

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Activity monitoring of a person for a long-term would be helpful for controlling lifestyle associated diseases. Such diseases are often linked with the way a person lives. An unhealthy and irregular standard of living influences the risk of such diseases in the later part of one's life. The symptoms and the initial signs of these diseases are common to the(More)
Email service providers have employed many email classification and prioritization systems over the last decade to improve their services. In order to assist email services, we propose a personalized email community detection method to discover the groupings of email users based on their structural and semantic intimacy. We extract the personalized social(More)
Frequent sub graph mining and graph similarity measures are fundamental and prominent graph analytical techniques. These techniques are often applied together in many graph mining techniques such as clustering and classification. However, these techniques suffer from long running times because frequent sub graph mining and graph similarity measures have(More)
The trust-aware recommender system (TARS) is a newly proposed trust-aware application. It is able to solve the data sparseness problem of the conventional recommender systems. One of the basic research challenges in TARS is to find the recommenders efficiently. Existing works of TARS use the strategy of random walk to find the recommenders, which is(More)
One of the most advantages of the Semantic Web is to augment the data with a well-defined meaning and linking between data by using the RDF ontology language. Today most of data are stored in relational databases. In order to reuse and infer this data on the Semantic Web, there is a need for converting the data stored in relational databases to the form of(More)
The crucial issue in many classification applications is how to achieve the best possible classifier with a limited number of labeled training data. Active learning is one method which addresses this issue by selecting the most informative data for training. In this work, we argue that the performance of active learning could be improved through carefully(More)
The κ-Nearest Neighbors ( κNN) query is an important spatial query in mobile sensor networks. In this work we extend κNN to include a distance constraint, calling it a l-distant κ-nearest-neighbors (l-κNN) query, which finds the κ sensor nodes nearest to a query point that are also at or greater distance from each other. The query results indicate the(More)
Existing models of the Trust-Aware Recommender System (TARS) build personalized trust networks for the active users to predict ratings. These models have reasonable rating prediction performances, while suffer from high computational complexity. One solution is to utilize the global rating prediction mechanism for TARS, in which an intuitive assumption is(More)