A deep neural network-based approach in tag recommender system to overcome users’ Cold Start

Recommender systems are used in various fields such as movies, music, and social networks. Recommender systems aim to provide attractive offers to users according to their performance in the system. The most popular recommender systems are content-based models and collaborative filtering methods. One of the most important challenges and problems in recommender systems is the challenge of users’ cold start. So far, various methods such as machine learning algorithms, optimization approaches, and statistical methods, have been proposed by other researchers in improving internet marketing strategy and overcoming the cold-start problem, which despite having numerous applications, still could not solve the start problem. This article will investigate the problem of cold start users’ by presenting a recommendation model based on a deep neural network and considering the problem of improving the internet (network) marketing strategy. In this article, the relevant simulation is done on the popular Movielens dataset, which is from 2015, and the evaluations of the methods presented on this dataset are compared