# New Vector-Space Embeddings for Recommender Systems
## 2 Related Work
### Word Embedding  
Word embedding [30] applications to Recommender Systems have recently been proposed. In such models, items or users are represented by vectors, which are learned from the data. The main goal is to have a low dimensional vector space that contains beneficial information for recommending items. To our knowledge, most of the work done in this area is based on the word2 vec model, introduced in [15; 16]. In [31], the authors propose an approach that embeds products into the vector space. The products' vectors are learned using a skip-gram model [16] such that products with similar contexts (purchases) have close vectors. Then, clustering of the products' vectors is performed using the K-means clustering algorithm. Moreover, the transition probabilities between clusters are modeled. Products with the highest cosine similarities from the highest probability clusters are used for recommendations. Furthermore, vectors are learned for users such that these vectors are updated to predict the products from the users' purchases. It is noted that user to product recommendations are less persistent than product to product recommendations due to the need to continuously account for the new user's purchases. The proposed model relies on email receipts data. In [32], an item embedding approach is proposed, extending the Prod2 Vec model introduced in [31]. The extension is based on incorporating items' metadata during the training phase. The approach is tested on a music dataset. In [33], a co-factorization model is proposed. The building blocks of the proposed model are matrix factorization and item embedding. The item embeddings are learned based on the word2 vec model introduced in [16]. The embeddings are learned using the user's consumed items in addition to co-occurrence counts, which are computed as the number of users who consumed both items. The model is tested using datasets for scientific papers, movies and songs. In [34], recommendations for venues and check-ins are presented for users. The model is based on word2 vec techniques: skip-gram and continuous bag of words (CBOW), introduced in [15; 16]. Vectors for items as well as users are learned. Moreover, three recommendation methods are proposed. The first method is based on recommending the most similar items to a given user by calculating the cosine similarity between the items' vectors and the user's vector. The second method finds the users similar (neighbors) to a given user and then the recommendation is made using these neighbors' preferred items. Finally, the third method combines the previous two methods. The neighbors are found, then the most similar items to both the given user and the neighbors are found and used for recommendation. The approach is tested using a Foursquare check-in dataset. In [35], a recommender system is proposed based on CBOW word2 vec [15] and GloVe [36]. The approach mimics these NLP models such that items are treated as words and users' sessions are treated as sentences. Vectors are learned for items where items' similarities are computed using the cosine distance. These similarities are used to perform recommendations based on the user's history. Moreover, at the recommendation phase the items' similarities are weighted with the user's ratings to obtain a ranked set of recommendations. The approach is tested on a movie dataset where a recall@20 measure is used for performance evaluation. The testing is performed by removing the last movie that the user has seen and uses the last k user's seen movies to recommend a set of 20 movies. If the recommended set includes the movie held out for testing, then a success is counted, otherwise a failure. Finally, the percentage of successes over all users is computed. The authors showed that the GloVe based model outperforms the word2 vec based model. In [37], an item-based collaborative filtering method is proposed. The method is based on the word2 vec skip-gram model with negative sampling, introduced in [16].