Proposing a New Hybrid Approach in Movie Recommender System

Due to the unprecedented growth of information, goods and services, a lot of application programs have been created in recent years to help the selection of goods and services to customers. One of the most important application programs are recommender systems that used to things as proposed movies,...

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Bibliographic Details
Published in:International journal of computer science and information security 2014-08, Vol.12 (8), p.40-40
Main Authors: Amini, Monireh, Nasiri, Mahdi, Afzali, Mahdi
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Applications programs
Classification
Computer information security
Customers
Decision trees
Electronic commerce
Filtering
Filtration
Hybridization
Methods
Motion pictures
Neural networks
Ratings & rankings
Recommender systems
Studies
Websites
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Summary:Due to the unprecedented growth of information, goods and services, a lot of application programs have been created in recent years to help the selection of goods and services to customers. One of the most important application programs are recommender systems that used to things as proposed movies, books, web pages, and E-Business, etc. Most of recommender systems used Collaborative filtering (CF) and or content based filtering (CBF) to provide suggestion for users. In this paper, a new approach is examined for better assessing the interests of customers. With the understanding of customer behavior, appropriate offer will be provided to customers. In fact, by the use of a new hybrid approach, weakness of content based filtering and Collaborative filtering methods will be resolve. First the authors reviewed the recommender systems and investigated the types of filtering. Then a new hybrid approach by using CF and CBF methods is presented in a movie recommender system. Results are evaluated on movielens valid data, showing improvement.
ISSN:1947-5500