Title: VECTORIZATION METHODS IN RECOMMENDER SYSTEM. Abstract: The most used recommendation method is collaborative filtering, and the key part of collaborative filtering is to compute the similarity. The similarity based on co-occurrence of similar event is easy to implement and can be applied to almost all the situation. So when the word2vec model reach the state-of-art at a lower computation cost in NLP. An correspond model in recommender system item2vec is proposed and reach state-of-art in recommender system. It is easy to see that the position of user and item is interchangeable when their count size gap is not too much, we proposed a user2vec model and show its performance. The similarity based on co-occurrence information suffers from cold start, we proposed a content based similarity model based on doc2vec which is another technology in NLP.