A Model for Movie Recommender Systems Based on Collaborative Filtering
Behnam Heydari, Elham Parvinnia · Communications on Advanced Computational Science with Applications · 2017
The recommender systems are used in different fields such as movie, music, social networks and etc. their aim is giving attractive suggestions to users concerning their performance in the system. Content-based methods and participatory filtering are the most popular recommender systems. The participatory method has two main problems. The first one is a cold start of users and the next problem is loyal users believe. In this article, one recommender system has been designed with a combination of the content-based and participatory filtering methods as a boosting system. Besides, that includes the problem solving of a cold start, trusting and attention to the loyal users. The recommender method consists of three steps: 1) the primary classification of all users and assigning a suitable classification to a new user. 2) The determination of an appropriate weight for each characteristic of slightly users and close neighbors of a new user. 3) Making a scores adjacency matrix of the neighbors close to available movie and the scores calculation of new users for each movie. The presented method, concerning demographic information of users, utilizes content-based system capability for primary classification. The gained consequences show that, mean absolute error and the root mean square error for the recommender method compare with Naive Bayes, C 2 4.5, C M 4.5 and RCA has improved about 8.4%.