Machine Learning
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A Content-Based Algorithm for Blog Ranking
ICICSE '08 Proceedings of the 2008 International Conference on Internet Computing in Science and Engineering
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Predicting mental health status based on web usage behavior
AMT'11 Proceedings of the 7th international conference on Active media technology
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Currently, people around the world are suffering from mental disorders. Given the wide-spread use of the Internet, we propose to predict users' mental health status based on browsing behavior, and further recommend suggestions for adjustment. To identify mental health status, we extract the user's web browsing behavior, and train a Support Vector Machine(SVM) model for prediction. Based on the predicted status, our recommender system generates suggestions for adjusting mental disorders. We have implemented a system named Web Mind as the experimental platform integrated with the predicting model and recommendation engine. We have conducted user study to test the effectiveness of the predicting model, and the result demonstrates that the recommender system performs fairly well.