A new item recommendation algorithm based on convolutional neural network

Due to the lack of label information for new items, the recommendation effect of traditional recommendation algorithms will be reduced. To solve this problem, this paper proposes a recommendation algorithm based on Word2Vec and convolutional neural network. First extract the keywords of the item text description information, use the Word2Vec model to convert the keywords into word vectors, calculate the similarity matrix of each keyword, and then use the similarity matrix as the input layer of the convolutional neural network to obtain the similarity between the items. Calculate the prediction score of the new item, and finally consider the user's preference for attribute information to generate recommendations. The experimental results show that the algorithm has a good effect on the hit rate.