# New Vector-Space Embeddings for Recommender Systems
###### Abstract  
In this work, we propose a novel recommender system model based on a technology commonly used in natural language processing called word vector embedding. In this technology, a word is represented by a vector that is embedded in an \(n\)-dimensional space. The distance between two vectors expresses the level of similarity/dissimilarity of their underlying words. Since item similarities and user similarities are the basis of designing a successful collaborative filtering, vector embedding seems to be a good candidate. As opposed to words, we propose a vector embedding approach for learning vectors for items and users. There have been very few recent applications of vector embeddings in recommender systems, but they have limitations in the type of formulations that are applicable. We propose a novel vector embedding that is versatile, in the sense that it is applicable for the prediction of ratings and for the recommendation of top items that are likely to appeal to users. It could also possibly take into account content-based features and demographic information. The approach is a simple relaxation algorithm that optimizes an objective function, defined based on target users', items' or joint user-item's similarities in their respective vector spaces. The proposed approach is evaluated using real life datasets such as \"MovieLens\", \"ModCloth\", \"Amazon: Magazine_Subscriptions\" and \"Online Retail\". The obtained results are compared with some of the leading benchmark methods, and they show a competitive performance. collaborative filtering; item recommendations; item vectors; recommender systems; spherical embeddings; word embeddings Article  
New Vector-Space Embeddings for Recommender Systems  
[PERSON], [PERSON], [PERSON], S.  
Item-item (item-based): items are recommended based on the similarity between them. The similarity computation between items depends on users who have rated/purchased both of these items. * _Content-Based:_ Recommendations are suggested based on the user's history of preferences and profile where the features of the items are taken into consideration. For example a user who liked or purchased a particular item with certain features/specifications and a certain price range is more likely to show interest in another item possessing similar features. * _Demographic:_ These systems categorize users and provide recommendations based on the demographic profile of the user, such as age, gender, country, occupation, and so forth. Users in the same demographic group tend to have similarities in their interests, as opposed to users in different demographic groups. * _Knowledge-Based:_ These systems use knowledge to construct relations between items and users' needs and preferences. The recommendations are suggested based on how the item is beneficial to a certain user. * _Community-Based:_ These systems make use of the information available in social networks. Recommendations can also be considered as \"people-to-people correlation\", but this time relying on the user's network of friends. * _Hybrid:_ These systems combine different approaches from those mentioned above. \"Collaborative Filtering\" (CF) is one of the most widely used Recommender Systems classes [5]. Because the user's past transactions and the item's past ratings and selling events may not be large enough to assess his preference for the item, other like-minded users' behavior and other resembling items will be used as proxies. In other words, users with similar taste are matched with the current user, their purchasing and ratings behavior is observed and, based on that, items are recommended to the user in question. In such models, novel items are also introduced to the user in order to discover new interests. It is well-known that less frequently sold items present the greatest challenge in Recommender Systems because of the dearth of purchasing precedence. This makes collaborative filtering a promising approach. Recommendations generated by CF fall into two classes: prediction or recommendation.