A Novel Approach To Solve Cold Start Problem In Sentiment Associated Content-Based Recommender System: K-L Divergence Method
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Abstract
E-learning has become a prominent part of education nowadays where recommendation system is an integral part of it. Recommender systems provide suitable course recommendations to an interested learner. But recommendation systems also have certain limitations. Cold-start problem is one such problem. In the case of a new course, generating the recommendations is very tedious task due to non-availability of past data related to that course. In such scenario, K-L divergence method is used to recommend the list of tentative students for newly launched course. Further, the overall sentiment of a student is used to boost the initial recommendations.
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