Nowadays, many online users find the selection of information and required products challenging due to the growing volume of data on the web. Recommender systems are introduced to deal with information overload. Cold start and data sparsity are the two primary issues in these systems, which lead to a decrease in the efficiency of recommender systems. To solve the problems, this paper proposes a novel method based on social network analysis. Our method leverages a multi-agent system for clustering users and items and predicting relationships between them simultaneously. The information on users and items is extracted from the user-item matrix as distinct graphs. Each of the graphs is then treated as a social network, which is further processed and analyzed by community detection and link prediction procedures. The users are grouped into several clusters by the community detection agent, which results in each cluster as a community. Then link prediction agent identifies the latent relationships between users and items. Simulation results show that the proposed method has significantly improved performance metrics as compared to recent techniques.

In recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a smart recommendation system that provides appropriate movies based on the user’s age range could be a useful tool for parents. Existing movie recommender systems use quantitative factors and metadata that lead to less attention being paid to the content of the movies. This research is motivated by the need to extract movie features using information retrieval methods in order to provide effective suggestions. The goal of this study is to propose a movie recommender system based on topic modeling and text-based age ratings. The proposed method uses latent Dirichlet allocation (LDA) modelling to identify hidden associations between words, document topics, and the levels of expression of each topic in each document. Machine learning models are then used to recommend age-appropriate movies. It has been demonstrated that the proposed method can determine the user’s age and recommend movies based on the user’s age with 93% accuracy, which is highly satisfactory.

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In recent years, we have seen an increase in the production of films in a variety of categoriesand genres. Many of these products contain concepts that are inappropriate for children andadolescents. Hence, parents are concerned that their children may be exposed to these products.As a result, a smart recommendation system that provides appropriate movies based on theuser’s age range could be a useful tool for parents. Existing movie recommender systems usequantitative factors and metadata that lead to less attention being paid to the content of themovies. This research is motivated by the need to extract movie features using informationretrieval methods in order to provide effective suggestions. The goal of this study is to propose amovie recommender system based on topic modeling and text-based age ratings. The proposedmethod uses latent Dirichlet allocation (LDA) modelling to identify hidden associations betweenwords, document topics, and the levels of expression of each topic in each document. Machinelearning models are then used to recommend age-appropriate movies. It has been demonstratedthat the proposed method can determine the user’s age and recommend movies based on theuser’s age with 93% accuracy, which is highly satisfactory.