An Improved Switching Hybrid Recommender System Using Naive Bayes Classifier and Collaborative Filtering

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Description/Abstract

Recommender Systems apply machine learning and data mining techniques for filtering un- seen information and can predict whether a user would like a given resource. To date a number of rec- ommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Collaborative filtering recommender sys- tems recommend items by identifying other users with similar taste and use their opinions for recom- mendation; whereas content-based recommender sys- tems recommend items based on the content informa- tion of the items. These systems suffer from scalabil- ity, data sparsity, over specialization, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain afore- mentioned limitations of these systems. In this paper, we proposed a unique switching hybrid recommenda- tion approach by combining a Naive Bayes classifica- tion approach with the collaborative filtering. Exper- imental results on two different data sets, show that the proposed algorithm is scalable and provide better performance–in terms of accuracy and coverage–than other algorithms while at the same time eliminates some recorded problems with the recommender sys- tems.