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Provide a Web Recommender System to Improve Search Quality Using DBSCAN Clustering Algorithms and SVM Machine Tracking Method

Molae Fard

Today, due to the increasing growth of web pages, the existence of a system that can extract the information needed by users from the huge amount of data available on the web seems necessary. To do this, we need to customize the systems in question. One of the best ways to customize your system is to use recommendation systems. Recommender systems are systems that can provide appropriate suggestions to the user by obtaining limited information from the user. Recommender systems can predict a user's future requests and then generate a list of the user's favorite pages. In other words, an accurate index of user behavior can be obtained and a page can be predicted that the user will select in the next move, which can solve the problem of cold start system and improve the quality of the search. In this article, a new method is proposed to improve the recommender systems in the field of web, which uses the DBSCAN clustering algorithm for data clustering, which achieves a 99% efficiency score. Then, using the Page rank algorithm, the user's favorite pages are weighed. Then, using the SVM method, we categorize the data and give the user a hybrid recommender system to generate a forecast, which will eventually provide the recommender with a list of pages that the user may be interested in. Evaluation of the research results showed that using this proposed method can achieve a score of 95% in the call section and a 99% score in the accuracy section, which proves that this recommending system can achieve up to 90%. Identify user pages correctly and greatly reduce the weaknesses of other previous systems.