## 1. Recommendation Models  
One of the main research lines in RSs is the development of novel models able to capture deeper and subtler information from the data, and to exploit it for offering more accurate and relevant recommendations. Many of these proposals are oriented to improve the heart of the RS, the so-called Collaborative Filtering (CF) method.  
Nowadays, the de facto standard in CF-based RS methods is Matrix Factorization (MF). It is based on the assumption that the preferences of users for particular items are characterized by a collection of hidden features, referred to as the latent factors. The mf method is responsible of unhiding these latent factors, of measuring them and, the most important part, of taking advantage of them to provide accurate and relevant predictions. For these reasons, the process of extracting and refining latent factors is a very active research area in RSs.  
This Special Issue includes important contributions that provide novel methods for capturing user information in new ways. In [1], the authors propose to include the temporal variable into the equation, giving rise to a time-aware recommender system. Thanks to that, the authors are able to track the evolution of the preferences of users with time, adjusting consequently the latent factors to provide up-to-date accurate recommendations. This is particularly relevant in the domain of music recommendation, where preferences of the users are very mutable and affinities evolve quickly with the prevailing musical trends.  
This attempt of providing highly customizable recommendations is a key idea in current RS research. This is based on the well-known fact about RS that they tend to supply 'average recommendations'. For instance, a RS issuing recommendations of movies will always consider that renowned films, like The Godfather, are good recommendations for a user, independently of his/her particular idiosyncrasy. To overcome this problem, in [2] the authors propose a model to alleviate this 'baseline course' by correcting the implicit bias of the system. For this purpose, the authors formulate a unified baseline estimation model based on the standard deviation of the user's features from the average system's features. This path toward specifically tailored recommendation is also explored in [3]. In this paper, the authors propose to add an extra cognitive layer to the standard predictive model. The task of this layer is to identify similar users according to their cognitive footprint. With this information, the system is able to refine the recommendations to fit better with users' tastes.  
This idea of capturing further semantic relations between the users and items of the rs is recurrent in this Special Issue. In [4], the authors propose to import knowledge graphs to rs, proposing a novel model called Neighborhood Aggregation Collaborative Filtering (NACF). It uses the knowledge graph to spread and extract the user's potential interest, and iteratively injects them into the user features with attentional deviation.  
In a way or another, this lack of customizability in the cf model is due to the linearity of the underlying mf method. The model produced by mf is essentially linear, which deeply compromises the expressivity of the model. This problem has been addressed in [5], published in this Special Issue, in which the authors propose to iteratively refine the predictions of the mf algorithm. In this way, the standard mf model is tweaked by performing subsequent factorizations that allow the model to estimate the expected suffered error, so that it can anticipate to it and compensate it. This idea mimics recurrent techniques in deep learning, in which the deep architecture allows the first layers to focus on the main features of the data, delegating a finer tuning of the results to the deepest layers.