Personalized recommendation method fusing multiple pieces of information

The invention discloses a personalized recommendation method fusing multiple pieces of information. The method comprises the steps of obtaining the similarity between a user and a project by adoptinga word2vec algorithm and an FM algorithm, obtaining the predicted click probability of the user and the project by adopting a RippleNet algorithm, obtaining a predicted score by adopting a dynamic fusion algorithm, and providing a personalized recommendation list for the user based on the predicted score. According to the invention, the knowledge graph and the comment content are used as multi-source data; the data is processed by using different algorithms, and effective combination is performed by using a dynamic fusion method, so that more accurate personalized recommendation services areprovided for users, a better recommendation effect can be achieved, and the problem of reduced recommendation accuracy caused by data sparseness can be effectively solved. 本发明公开了一种融合多信息的个性化推荐方法，该方法包括采用word2vec算法和FM算法获得用户与项目的相似度