## 1 Introduction  
Recommender Systems (RS) [1] are machine learning-based personalization applications. They facilitate human/machine integration by providing accurate recommendations of items to users; mainly, items are products or services recommended to collaborative clients. Remarkable commercial companies that incorporate RS are Spotify, Netflix, TripAdvisor, and Amazon. RS can be classified according to their filtering strategy: demographic [2], content-based [3], context-aware [4], social [5], collaborative [6] and different ensembles [7]. Of the mentioned filtering approaches, the Collaborative Filtering (CF) is the most relevant since it returns the most accurate predictions and recommendations. The first CF implementations made use of the memory-based K-Nearest Neighbors (KNN) algorithm [8] due to its simplicity and because it conceptually fits with the recommendation task. Nevertheless, the KNN algorithm has some drawbacks when applied to CF RS: it is not accurate enough and it is not efficient, since successive executions are necessary to make successive recommendations. For these reasons, the KNN approach was replacedby model-based methods, such as Matrix Factorization (MF) [9], non-Negative Matrix Factorization (NMF) [10] and Bayesian NMF (BNMF) [11]. MF models generate hidden factors for both users and items. Hidden factors can be considered as embedding representations. The rating prediction of each item to each user is obtained by just making the dot product of both (user and item) embeddings. MF models are not specifically designed to plot visual representations, but their embedding values can be processed to provide recommendation explanations [12] and to draw relationships [13]. Currently, MF models are being replaced by neural networks (NN) approaches, and consequently, hidden factors are replaced by neural embedding layers. The most relevant NN models in the CF area are DeepMF [14] and Neural Collaborative Filtering (NCF) [15]. DeepMF makes a deep learning implementation of the MF model; thus, it contains a user embedding layer, an item embedding layer, and a 'Dot' layer to join the preceding embeddings. NCF replaces the DeepMF 'Dot' layer with an MLP and eventually combines deep and shallow learning. Embeddings are abstract, low-dimensional representations of information. There are a large number of fields where embeddings are used to encode data structures; network embeddings are largely applied to graphs [16], where they embed entities and relationships in low-dimensional spaces [17]. Gene sequences have been predicted from embeddings [18], and biomedical networks [19] have been evaluated as social graphs. Social communities are detected in the embedding-based Silhouette [20], via clustering of network node embeddings. Image applications are also a recurrent target for embedding processing, such as the person identification based on pose invariant embedding [21], image tag refinement through deep collaborative embedding [22], and handcrafted image retrieval [23] using supervised deep feature embedding. Natural language processing is also an area where tokens must be coded through different types of embedding models. Beyond the most known word2 vec model, there are specific models, such as the convolution-deconvolution fusion word embedding [24], which makes a fusion of context and task information. Finally, fairness in RS is improved by means of an embedding-based combination of MF and deep learning models [25]. In the CF area, embedding layers have been used to implement autoencoders, such as the probabilistic autoencoder in [26] fed with the user-item data, the combination of stacked convolutional autoencoders, and stacked denoising autoencoders [27] to extract knowledge in RS. Context-aware information is coded using a deep learning autoencoder [28] that predicts scores and extracts features.