A Collaborative filtering Method based on Forgetting Theory And Neural Item Embedding

The collaborative filtering algorithm (CF) is one of the most important algorithms in the recommendation system. Recently, the use of neural word embedding methods to learn the latent representation of words has become a mature method in the field of natural language processing (NLP). Inspired by the SGNS algorithm in the NLP field, we propose f-item2vec as a new method to learn the latent representation in item vector space, and based on this vector to calculate the similarity between items. In addition, according to the forgetting process, we propose a new user preference model to accurately identify the user's short-term preferences and long-term preferences. The experimental results show that the proposed method is effective and superior to the traditional algorithm in predicting the score and generating the recommendation list.