An Effective Algorithm to Improve Recommender Systems using Evolutionary Computation Algorithms and Neural Network

Abstract

Abstract The growing Internet access and easy access to it have resulted in a significant increase in e-content, which, along with many benefits, has caused problems for users. Internet users simply cannot find the content they need from this massive amount of data. Users are faced with a lot of suggestions for choosing goods, buying items, selecting music and videos, and more. Advantage systems can be used to overcome these problems. Today, with the spread of people's use of cyberspace, such as web sites and social networks, and increasing the need for conscious and clever selection of people, recommender systems has been extensively investigated. Although the neural network can identify the connections between the inputs and outputs of a dataset, but in order to achieve the proper performance of the neural network, a proper structure should be considered. We will use the mantle algorithm to determine this structure. The mantle algorithm is a form of traditional genetic algorithm that uses local search to reduce the time to achieve optimal response. Genetic algorithms are created to search across the search space, while the local search, the neighborhood of the neighborhood, finds every response found by the genetic algorithm to find better answers. This algorithm seeks to find the optimal values ​​for the parameters of the neural network method, so optimal solutions of the memetic algorithm is considered to be used to set parameters for the neural network method. The results of this study show the desirable performance of the proposed approach in this study.