A Web Page Recommendation using Naive-Bayes Algorithm in Hybrid Approach
Keywords:
Naive-Bayes Classifier, Content Based Filtering, Collaborative FilteringAbstract
Web page recommendation has been emerging as a most important application area in mining. In order to predict the users’ interests for effective recommendation two methods such as collaborative filtering and content based filtering are considered. Content based filtering is applied by considering information including user’s profile and the users’ past preferences. User preferences and similarity with other users are considered as primary factor in collaborative filtering method. In probabilistic generative the unobserved user preferences are also considered along with ratings and semantic content. To improve the accuracy and to still improve the user satisfaction this paper applies Naïve- Bayes classifier along with content and collaborative based approach. Naive-Bayes classifier is considered to be more efficient as it considers dynamic and adaptive features for accurate classification. The features that are considered in Naive-Bayes classifier are independent to each other. The performance of the proposed algorithm is measured using the precision and recall.
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