CSEIT172447 | Received : 12 July 2017 | Accepted : 23 July 2017 | July-August-2017 [(2)4: 141-147] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2017 IJSRCSEIT | Volume 2 | Issue 4 | ISSN : 2456-3307 141 A Web Page Recommendation using Naive-Bayes Algorithm in Hybrid Approach 1 S. Abirami, 2 J. Bhavithra, 3 Dr. A. Saradha 1,2 Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, India 3 Department of Computer Science and Engineering, Institute of Road and Transport Technology, Erode, Tamunadu, India ABSTRACT 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. Keywords : Naive-Bayes Classifier, Content Based Filtering, Collaborative Filtering I. INTRODUCTION Web mining is the process where the information is extracted from the web and it can evaluate the effectiveness of particular web site. The information on the web has been increasing, where recommendation should be made effectively. In early days few companies were generating data and others were consuming. Nowadays, all of us were generating data and all of us were consuming. The web mining requires the recommendation system which extracts the required knowledge from the correlated data, since the size of the data is relatively high on the web. [20] Web recommender system is one which it provides list of web pages that are mostly liked for the users’. The recommender system compares the similar and the dissimilar data among the other content for the effective recommendation. Recommender system gives the list of recommendation using one of content and collaborative based filtering. First, content based approach the recommendation is made using the users’ information from their own profile and according to their own interests. For instance, the user likes the web service sa then the services related to that service will be recommended. In Collaborative filtering, the recommendation is made using the interests of other users’ having the similar preferences. For instance, if the user likes the service sa and sb. There will be many other user who likes the service sa and sb and also like the service sc. Probably the service sc will be recommended to that user. [19] The main goal of this paper is to reduce the complexity of handling data and to increase the web service recommendation, which reduces the users’ work on giving their preferences (e.g., the user interests and their personal information) and to satisfy their needs. The cold start problem is solved where the user’s interests is identified without any information given by user. The accuracy level of prediction for