# Applying Recommender Systems to Predict Personalized Film Age Ratings for Parents
## 2 Related Work
### Recommender Systems
Matrix factorization [26], a widely used dimensionality reduction technique, characterizes both items and users using vectors of latent factors inferred from item rating patterns. The degree of correlation between the latent factors of users and items drives the generation of recommendations. SCoR [15] assigns synthetic coordinates to users and items (nodes), as proposed in [24; 26]. However, instead of relying on the dot product, ScoR measures the Euclidean distance between a user and an item. Once the system converges, the distance between a user-item pair effectively predicts the user's preference for that item. The ScoR framework offers several advantages: It eliminates the need for parameter tuning to achieve high performance and is more resilient to data sparsity than many other frameworks. Various artificial neural network architectures have also been used in model-based recommendation systems [16; 27]. Convolutional neural networks (CNNs) [28] have been utilized to process the output of pre-processing steps, such as the outer product of user and item ratings to generate a 2D interaction map. This approach enables the modeling of user-item interaction patterns and the capture of high-order correlations. More recently, advancements in graph neural networks (GNNs) have leveraged embedding propagation to iteratively aggregate neighborhood embeddings. By stacking propagation layers, each node can access information from high-order neighbors, surpassing traditional methods that are typically limited to first-order neighbors [27]. In [29], a graph convolutional matrix completion approach was introduced, utilizing a graph autoencoder framework based on a differentiable message passing on a bipartite interaction graph. This method, combined with a bilinear decoder, predicts new ratings in the form of labeled edges. Additionally, the graph autoencoder framework is naturally extendable to incorporate side information for both users and items. In [16], neural network architectures were investigated for collaborative filtering. The authors proposed a general framework with three specific implementations: GMF, MLP, and NeuMF, which model user-item interactions in distinct ways. This work complements mainstream shallow models for collaborative filtering, opening new avenues for recommendation research utilizing deep learning. Content-Based Recommender Systems analyze items (or their descriptions) to construct item representations and user profiles, which are then used to recommend new items to users [30]. The recommendation process involves matching user profile attributes with those of content items, resulting in an evaluation of user interests in specific items [31],which forms the basis for recommendations. Most content-based recommender systems rely on textual features to represent items and user profiles. Additionally, various hybrid methods combine multiple approaches to enhance recommendation performance [32]. This paper is structured as follows: Section 2 reviews the related work for age rating systems and recommender systems. Section 3 introduces the main problem formulation surrounding a personalized film age rating (PFAR) that we study in this paper. Section 4 presents the proposed framework for the PFAR problem based on parental age ratings. Section 5 describes the experimental results. Finally, our conclusions are presented in Section 6.