A recommender system that represents items in a catalog by first feature vectors in a first vector space based on first characteristics of the items and second feature vectors in a second vector space based on second characteristics of the items different from the first characteristics and maps a feature vector defined in the first vector space for an item to a vector in the second vector space to provide recommendations based on the item.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of recommending items to a user, the method comprising: representing an item by a first feature vector in a first vector space based on first characteristics of the item; mapping the first vector to a second vector in a second vector space in which feature vectors representing items are based on second characteristics different from the first characteristics; and using a relationship between the second vector to another second vector in the second vector space representing another item to determine whether or not to recommend the other item to the user. 2. The method according to claim 1 and comprising using a neural network to map the first feature vector to the second feature vector for the item. 3. The method according to claim 2 and comprising training the neural network on a plurality of training items having known first characteristics and known second vectors to generate second vectors responsive to the first characteristics that satisfy a cost function criterion. 4. The method according to claim 2 and using the neural network to generate the first feature vector for the item. 5. The method according to claim 4 wherein the first characteristics comprise content based (CB) characteristics. 6. The method according to claim 5 wherein the CB characteristics comprise text descriptive of the item. 7. The method according to claim 6 wherein the neural network represents words in the descriptive text by word2vec (w2v) vectors. 8. The method according to claim 7 wherein the neural network uses the w2v vectors to process the descriptive text as a bag of words (BOW). 9. The method according to claim 7 wherein the neural network arrays the w2v vectors in a matrix by sequence of appearance of the words they represent in the descriptive text. 10. The method according to claim 9 wherein the neural network processes the matrix with a convolution neural network (CNN). 11. The method according to claim 5 wherein the CB characteristics comprise tag data associated with the item. 12. The method according to claim 5 wherein the content based characteristics comprise numerical data associated with the item. 13. The method according to claim 2 and comprising using the neural network to generate the second feature vector for the other item. 14. The method according to claim 2 and comprising using a neural network different from the neural network that maps the first feature vector of the item to the second feature vector for the item to generate the second feature vector for the other item. 15. The method according to claim 1 wherein the second characteristics comprise data that reflects frequencies with which the item is associated with other respective items. 16. The method according to claim 15 and comprising using a neural network similar to a skip gram neural network to generate the second feature vector of the other item. 17. The method according to claim 16 wherein the neural network similar to the skip gram neural network comprises an item2vec neural network. 18. The method according to claim 15 and comprising generating the second feature vector for the other item by matrix factorization of a ranking matrix. 19. A recommender system for recommending an item to a user, the recommender system comprising: a database comprising a plurality of catalog items characterized first characteristics and different second characteristics and having second feature vectors in a second vector space that represent the catalog items based on the respective second characteristics of the catalog items; and a processor configured to: receive first characteristics for an item; generate a first feature vector in a first vector space different from the second vector space based on the received first characteristics; map the generated first feature vector to a second feature vector for the item in the second vector space; and use the mapped second feature vector and second feature vectors representing items in the catalog to recommend an item from the catalog to the user. 20. The method according to claim 19 wherein the first characteristics are content based characteristics and the second characteristics are collaborative filter characteristics.