A Comparative Study to Enhance the Performance of Web-Page Recommendation System

Efficient web browsing demands effective aid in searching anticipated content. Recommender systems are prodigious source of providing assistance to the internet users. Diverse algorithms are in practice to give recommendations. All recommender systems work on policy of scrutinizing behaviors of users. They find neighbors of each user to give final recommendations. Similarity Matrix computations outcomes in developing the recommendations for each user. However, accuracy of recommendations depends upon the correctly made predictions. Hence, web page recommendation system demands improvements in the traditional practices. This paper verdict the differences in results for user based collaborative filtering recommendation strategies and clustering users before implementing recommendation algorithm for web page recommendation system. Results are compared through computation of RMSE. Differences in results suggest that accuracy of recommendations is improved if clustering-based approach is applied for recommendation of web pages. This study gauges the results on the web log data set to recommend users the required content on the internet by evaluating the browsing behavior with PCC similarity measure.

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