An effective web page recommender system with fuzzy c-mean clustering

With the exponential development of the number of users browsing the internet, an important factor that now the developer community is focussing on is the user experience. Recommender systems are the platforms that make personalized recommendations for a particular user by predicting the ratings for various items. Recommender systems majorly ignore the sequential information and rather focus on content information, but sequential information also provides much information about the behavior of the user. In this research work, we have presented a novel web-based recommender system which is based on sequential information of user’s navigation on web pages. We received top-N clusters when Fuzzy C-mean (FCM) clustering is employed. We determined the similar users for the target user and also evaluated the weight for each web page. We have tried to solve that problem of recommender systems as we offered a system to forecast a user’s next Web page visit. In our work, we proposed a system which generates recommendations to the users, by considering the sequential information that exists in their usage patterns of Web pages. We employed fuzzy clustering to give recommender system a sequential approach. We calculated weights for each page category considered in our system and predict top page recommendation for the target user. The real-world dataset of MNSBC is used in the experiments. The dataset consists of 5000 user entries with 6, entries per user. When we performed a comparison between the existing model with our proposed model, then it clearly showed that the accuracy of the proposed model is almost three times better than some existing systems. The accuracy of our proposed model is nearly 33 %.