Research on application of article recommendation algorithm based on Word2Vec and Tfidf

With the rapid expansion of the amount of information in social media such as various news and information software, people urgently need to realize the automatic classification of this information to help users quickly find the information they need and filter spam. Aiming at the curse of feature dimension and non-semantic features in traditional text classification models, this paper studies the classification of article text based on the Word2vec model. One of the shortcomings of the Word2vec model is that the essentiality of words in diverse texts is not the same, so this paper introduces the TFIDF model, which can weight Word2vec word vectors to achieve a weighted Word2vec classification model. At last, the weighted Word2vec and TFIDF models are combined.