Improved Recommender Systems Using Data Mining

Document Type : Original Article


1 Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj branch, Islamic Azad University, Yasouj, Iran

2 Department of Computer Engineering, Faculty of Technology and Engineering, Qeshm Branch International University, Qeshm, Iran

3 Department of Accounting, Faculty of Accounting, Yasouj branch, Islamic Azad University, Yasouj, Iran.


Today, in order to buy goods through the Internet, every company or production organization has an internal commercial software site based on which it offers its products and services to customers. To ensure that the user has the ability to provide a suitable proposal for a request or to solve a need in the midst of a huge amount of data, recommender systems are the right solution. Different methods of providing suggestions in recommender systems are divided into eight methods according to the data mining of the classification of methods. In each method, in these systems, the necessary suggestions and predictions are provided to users in a special way, and the most important method is the recommender system among other methods , is the filtering method. In this method, the number of clusters in the data set related to recommender systems is dynamically determined by c3m clustering algorithm on a data set called Movielens ml-100k, which has a data oscillator in four inputs, as well as k-means algorithm and performance optimization. It was estimated. The final clustering is done well with the help of this method, if the target user enters after the clustering operation and by matching the profile information which includes a series of items rated by similar users in the same cluster, the similar cluster search for Based on the correlation filter, which is one of the methods used by the KNN algorithm, it finds its similar cluster with each cluster head (cluster representative) and based on the items ranked in demographic information, the nearest neighbors (neighbor and similar ) finds users-items and the item that has the highest rank among other users. The obtained similarity is stored in the user's top-n list and presented in the form of an offer.


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