Collaborative Filtering Based Model for Privacy-Preserving Course Recommendation
Date Issued
2015
Date
2015
Author(s)
Lee, Eric L.
Abstract
University students have to register for courses and usually there are many of those to choose from. It is time consuming for students check the course information for all courses before registration. As a result, this thesis proposes a recommender system to recommend courses to students based on the previous registration data of others. The advantage of our model is twofold. First, different from the previous works that require meta data about students or content information about courses, our model only needs the binary registration record of students for each course, thus protects the privacy of data provider. Second, different from the previous recommendation model that assumes items are independent, our model considers the courses-taken as a non-iid behavior to boost the performance. The experiment results show significant boost in our model comparing with the traditional recommender systems.
Subjects
Machine Learning
Recommender System
Artificial Intelligence
Data Mining
Education
Type
thesis
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