Research On Mean Model Of Recommendation System Based On Deep Learning

With the rapid development of the Internet,the amount of information on the Internet is more and more.The system that can bring accurate recommendation for users can greatly improve the user experience.Personalized recommendation algorithm has become a hot topic of research.Collaborative filtering is the most widely used recommendation algorithm.It analyzes and recommends users' interest according to the user's historical behavior data,but there is a serious problem of data sparsity in its research,and the existing algorithms are not enough to consider the real time of the recommendation,which are all the problems to be solved urgently.In this paper,the current recommendation algorithm is studied,and the current research status of the recommendation system is analyzed.At the same time,the application of deep learning in the field of recommender system is deeply analyzed.It is intended to apply the capability of the automatic feature extraction of stacked denoising auto-encoder in deep learning to the recommendation system to alleviate the problem of data sparsity,meanwhile,it can improve the real-time recommendation performance of recommendation system.To achieve this goal,there are many researches done in this paper.(1)Basic theory researchThis paper first introduces several typical recommendation algorithms,and focuses on collaborative filtering recommendation algorithm,and introduces user based collaborative filtering recommendation algorithm and project based collaborative filtering recommendation algorithm in detail.Secondly,severaltypical deep learning frameworks are introduced,and several typical recommendation systems based on deep learning are listed.(2)Incremental mean model and its improvementThrough the analysis of the mean model and its incremental expansion proposed by our group,we find that the problem of user interest drift is not considered when the similarity calculation is carried out.At the same time,there are some limitations when using the traditional kNN method to select the nearest neighbor object set.In this paper,a dynamic selection time mean model is proposed in this paper.The model introduces the nearest neighbor factor and the time function,uses the dynamic selection method to select the nearest neighbor set of the project,and uses the time function to improve the incremental mean model under the time weighting.(3)Time mean model and its improvementDeep learning has powerful feature extraction ability.This paper describes the working principle of Stacked Denoising Auto-Encoder(SDAE)in detail.And it is used to learn the deep feature representation of users(projects)in collaborative filtering recommendation.But SDAE can't make good use of the rating's time property.The time mean model proposed in(2)has a good ability to deal with large data and real-time recommendation performance.However,in the process of compression vector,information distortion will occur because of the existence of data sparsity.In view of the above problems,a hybrid recommendation algorithm based on SDAE and time mean model is proposed,which makes full use of the automatic feature learning ability of SDAE and the fast calculation ability of time mean model in large data.It makes up for the shortcomings of both of the two.(4)Implementation of the improved mean modelThis paper carries out experiments on 3 standard data sets,verifies a)the importance of time attributes in real-time recommendation;b)the necessity ofdynamic methods when select the nearest neighbor set of users(projects);c)the capability of the automatic feature extraction of SDAE by comparing traditional methods and the methods proposed in this paper.The experimental results show that the two improved models proposed in this paper further improve the recommendation performance of the recommender system.