## 2 _Theoretical Advances towards New Recommender Systems_  
One of the main research lines we have investigated in this Special Issue is the development of new RS models and technology. These novel approaches have allowed us to address old problems in radically new ways. Some of the works collected in this issue have proposed disruptive changes in the nature of the prediction algorithm. In this direction, in [1], the authors introduce the novel concept of 'black sheep neighbors', which are groups of similar users that have a discordant opinion with respect to the general consensus about controversial items. Enhancing the role of these black sheep neighbors, the new models can outperform the baselines in terms of accuracy and efficacy. In a similar vein, in [2], the authors propose to import techniques from natural language processing to the RS world. In particular, they propose to apply a relaxation algorithm to handcraft a more sophisticated vector embedding of users and items into the latent space. The accuracy of these embeddings is known to be crucial for the success of the RS, since they aim to capture, in only a few factors, the essence of a user and an item. Only if the factors are well computed do the comparisons between users' and items' factors accurately characterize the affinity, which is a necessary condition for achieving a good performance for these methods. Hence, in [2], these techniques inspired by the classical word vector embedding are used to provide a new versatile framework to compute these latent factors, leading to a compelling performance in real-life multi-purpose datasets. This idea of exploiting the similarities between users has also been studied in [3]. There, the authors propose an innovative solution to incorporate relevant user feedback to improve the CF process. Inter-coder agreement techniques are also used to evaluate the performance of the suggested method in the context of hotel recommendations, with a clear improvement with respect to the state of the art. Following these ideas of studying the intrinsic similarities between users, in this Special Issue, we have included paper [4]. In this paper, a novel sampling criterion for neighbors is introduced to speed up the training of Graph Convolutional Neural Networks (GCNNs) applied to RSs. Despite the effectiveness and accuracy of GCNNs, their computational complexity makes them unfeasible for tasks in real-world RSs. Instead of using the classical recursive approach, in this paper, the authors apply Kullback-Leibler divergence to compare the interaction probability distribution of the vertices. The results demonstrate a noticeable drop in the computational time while preserving the accuracy of the model. Other graph-based approaches to RSs have been explored in this issue. For example, in [5], the authors propose to use an attributed graph-based representation to encompass several user features in location recommendation problems. Using this additional information, the RS is able to provide a much more accurate latent factor embedding in these highly sparse settings. The empirical results support these findings, with a substantial improvement in the accuracy for multiple localization recommendation datasets. Another graph-based RS was explored in [6]. In this case, a knowledge graph approach is taken to enhance the attention aggregation network. Thus, the authors propose the KANR model, which is composed of three stages: an attention aggregation network to gather user features, a knowledge graph embedding to push these features into a latent space, and a final fusion component to provide the final predictions. Finally, in this Special Issue, we have also explored new ways of looking at the prediction problem from a regression point of view. In [7], a novel approach using the Dirichlet distribution was developed. Classically, the prediction problem for rating filling is addressed in an RS as a classification problem, in which one of the possible rating values must be assigned to each unknown rating.