# New Vector-Space Embeddings for Recommender Systems
###### Abstract
The first one attempts to predict the rating that the user is likely to give to a certain item. Recommendation is concerned with the top k items that the user is likely to be interested in or purchase [6]. Conventional approaches for CF include matrix factorization and neighborhood-based collaborative filtering. Matrix factorization [7] is based on building a user-item interaction matrix to which dimensionality reduction techniques such as Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) are applied. In neighborhood-based CF, user-item interactions are directly used (based on neighbors or similar items/users) to predict interest for new items [8]. Moreover, recent research in the field of recommender systems has adopted a number of techniques, including deep learning [9; 10; 11; 12]. Different architectures of neural networks are proposed such as multilayer perceptron (MLP), convolutional neural network (CNN) and recurrent neural network (RNN). The non-linearity in data can be effectively modeled by deep neural networks through non-linear activations. This allows the capturing of complex and detailed user-item interaction patterns. Deep learning is also beneficial in learning useful representations from input data, specially where in real applications descriptive information about items and users is accessible. Interface optimization is also introduced in recommender systems where visual aspects are taken into consideration [13; 14]. In this work, we propose the use of a novel technology called word vector embedding for the Recommender Systems problem. This technology was introduced and was further developed in the natural language processing field. In word vector embedding, words are represented as numerical vectors where the vectors' positions in an \(n\)-dimensional space convey the words' semantics. Words of similar meanings will have vectors near each other from the distance point of view. In other words, the vector encodes the corresponding word's meaning together with all its semantic relations with similar and dissimilar words of the corpus. Recommender systems are also based on similarities (items to items and users to users). They seem to be promising candidates for the application of vector embedding systems. Users can be represented as vectors that embody all their likings, interests and demographics, with similar-minded users having vectors that are nearby in the space. In other words, the vector encodes the type of person and his preferences. Similarly, an item or a product is a complex entity that is described by several features, for example purchase patterns, ratings, product description and specification, and common preferences among similar users. All this makes simple feature vector modeling not straightforward, and this calls for vector embeddings that are learned using intricate similarity modeling by careful placement of the vectors. In the embedding approach, the item or product is modeled as a vector that encodes the intrinsic features, values and preferences of this item, where these features are encoded over all of its components in a distributed fashion. The application of word embeddings to recommender systems is a fairly new idea. Only a few works have simultaneously applied this idea recently. The goal of this research is to explore the vector embeddings as a potential addition to the arsenal of other existing technologies, The other goal is to introduce a novel vector embedding that makes this technology applicable to more Recommender Systems problem types. Conventional word vector embeddings, such as the word2 vec method [15; 16], are trained on context or co-occurrence data. This means that they operate on a basket of purchased items (for a specific user) and from that they infer the similarities between items and possibly provide a recommendation. Therefore, they are not applicable to the problem of learning user ratings, because this basket of elements does not exist and only ratings of items by users do.