DESIGN OF A CONTEXT-AWARE RECOMMENDER SYSTEMS FOR UNDERGRADUATE PROGRAM RECOMMENDATIONS

摘要

There are a variety of undergraduate programs available in the education system. Therefore, designing a recommender system based on academics performance alone may not be always helpful. Thus, there is a high demand for knowing other contextual information of the user which can influence the efficiency of the recommender system. Intelligent systems can be designed to predict the contextual information about the user. Selecting the most appropriate undergraduate program by considering different contextual parameters is highly needed for students who have passed class 12. This research work is to design a context-aware recommender system, which recommends under-graduate programs to students of class 12 based on the academic performance and with contextual parameters like financial background, Knowledge level, Group, interested-subject and interested-profession using collaborative filtering approach. This research paper proposes a novel method for creating a rating matrix and the identification and processing of contextual information in an efficient manner. This Context-aware Recommender system is designed based on the predictive values for the various contextual parameters using a contextual modeling approach. Implicit ratings are calculated using the collaborative approach. The results indicate that context-aware recommender engine is more efficient in generating the recommendations thereby improving the user satisfaction level.