# Applying Recommender Systems to Predict Personalized Film Age Ratings for Parents
## 2 Related Work
### Recommender Systems  
Recommender systems (RSs) gather information about user preferences for a set of items by collecting user ratings or monitoring user behaviors across various sources. This information is then used to generate predictions and provide personalized recommendations for items [14; 15; 16; 17]. RSs have become increasingly popular in helping users make decisions more efficiently by leveraging diverse methods [18]. RSs have been successfully applied across a wide range of domains, including e-commerce products, web pages, news articles, social networks, movies, music, hotels, television shows, books, restaurants, and even social connections [17]. The problem that a recommender system aims to solve is as follows: Given a set of users (\(U\)), a set of items (\(I\)), e.g., movies, products, etc., and a set of ratings (\(R\)), e.g., evaluations, provided by users for items, the objective of a recommender system is to predict the rating for a user-item pair that is not present in \(R\). In a typical RS, users create accounts and provide ratings for various items. Generally, as the number of users and ratings increases, the system improves its ability to deliver accurate predictions. However, when the available ratings data are extremely sparse (the sparsity problem) or when the system lacks information about the preferences of new users (the cold start problem) [19], most recommender systems struggle to provide reliable predictions. The primary function of RS is to predict the preference level that a user might have for a given item. The literature offers a wide range of techniques for recommender systems. Below, we provide a concise overview of the most prominent ones. Broadly, recommender systems can be categorized into two primary types: collaborative filtering and content-based approaches. Collaborative filtering (CF) relies solely on user preferences (e.g., ratings) for items to generate recommendations. In contrast, content-based recommender systems incorporate additional attributes related to both items and users (e.g., movie genres, content types,user demographics, etc.) to make suggestions. CF methods [20; 21; 22] analyze historical user behaviors and preferences to predict future interests. While these systems often face challenges such as data sparsity and the cold-start problem, they benefit from leveraging existing user-generated information, which is automatically collected as users interact with and rate items. Typically, user preferences are represented as numerical values within a predefined range. In memory-based CF approaches, recommendations are derived by correlating information through one of the following strategies [17]:  
1. Item-to-item correlation: Recommendations are based on similarities between items, utilizing their properties and associations. 2. User-to-user correlation: Suggestions are generated based on demographic similarities among users. 3. User-to-item correlation: Recommendations arise from matching users with items they have previously rated or expressed a preference for. Model-based collaborative filtering recommendation systems employ a variety of techniques to construct predictive models, which are then used to provide recommendations. These approaches often leverage dimensionality reduction techniques (e.g., [23]), where latent variables are introduced to capture and explain co-occurrence patterns in the data. In this context, each user or item is represented as a vector, with the user's vector encoding their set of ratings across all elements in the system. However, the inherent sparsity of these vectors makes it challenging to identify correlations between user-item pairs. To address this, various dimensionality reduction methods are utilized, such as singular value decomposition (SVD) [24], principal component analysis, probabilistic latent semantic analysis, and latent Dirichlet allocation [25].