融入注意力网络的深度分解机推荐算法.

Recommender system can find information that satisfies the user′s individual needs from huge amount of information. With the development of deep learning, deep learning has been widely applied in recommender systems. CTR prediction plays an important role in recommender system and has been widely used in many fields such as personalized recommendation, information retrieval, online advertising and so on. For the issue of large and sparse data in recommender system, this study merges xDeepFM model with attention network, and proposes a new CTR prediction model based on deep learning, which is called Atte-xDeepFM model. This model can solve the issue of feature scarcity, effectively learn the interactions relationship between features, and does not need to manually extract useful information in feature engineering. The comparative experiments on Avazu and Criteo data sets prove the effectiveness of the proposed model. Compared with the algorithm model commonly used in CTR prediction, the proposed model has better recommendation effect. [ABSTRACT FROM AUTHOR]