Research And Application Of Hybrid Recommendation Algorithm Based On Collaborative Filtering And Deep Learning

With the continuous development of information technology in modern society,traditional industries such as catering,news and shopping have found their own carriers in the Internet,and people's lives have undergone earth shaking changes.However,as the phenomenon of information overload on the Internet becomes more and more serious,people find it more and more difficult to dig out the content they are concerned about from the massive information on the Internet.In order to better meet the needs of users and tap their consumption potential,personalized recommendation algorithms emerge as the times require.At present,recommendation system has been widely used in various fields of the Internet,and has brought huge profits to many enterprises.According to Amazon's statistics,35% of its sales revenue is due to the use of recommendation system,which has also attracted many Internet practitioners and scholars to devote themselves to the research of recommendation system.Recommendation algorithms make predictions on ratings based on user item characteristics and user behavior.However,with the development of the Internet industry and the proliferation of platform users,traditional recommendation algorithms cannot fully extract the characteristics of users and items,and the recommendation effect is not ideal.In recent years,some scholars have continuously applied deep learning to the field of recommendation algorithms.Because the deep learning model has strong learning ability and can dig out more hidden information in the data,the personalized recommendation system combined with deep learning has become the main research direction of recommendation algorithm technology.Data sparsity and user interest migration have always been the problems faced in the field of recommendation algorithms.In order to solve the above problems,this paper proposes a hybrid recommendation algorithm based on collaborative filtering and deep learning,and designs a personalized film recommendation system based on the hybrid recommendation algorithm.The main work and innovations of this paper are as follows:Firstly,considering that the user interest will change with time,a user-item interest tag that sharing vector space is designed to ensure the dynamics of recommendation results.At the same time,the convolutional neural network is combined with the attention mechanism,the attention mechanism is used to analyze the user's interest in different types of items,the convolutional neural network is used to establish the high-order feature interaction between users and items,and an attention based convolutional neural network(ACNN)is proposed,which has stronger model interpretation ability and stronger information synthesis ability,It adopts explicit and implicit joint input,which also greatly alleviates the data sparsity.Then,through the comparative test with Mean,Auto Rec,PMF and NFM models on Movielens-1M and Movielens-100 k film data sets,it is verified that the ACNN algorithm has a great improvement in RMSE and MAE evaluation indicators compared with other mainstream models.Secondly,aiming at the label attribute of film type,a movie recommendation algorithm based on the preference feature of film type is proposed,in which the user's preference for movie type and the correlation degree between movie and movie type are modeled,and then the similarity between different films is calculated to predict the user's interest in unknown movies.Then,the ACNN algorithm and the recommendation algorithm based on the preference characteristics of movie types are applied to the Top-N recommendation situation at the same time.The weighted combination of the two recommendation algorithms obtains the final movie recommendation list.Taking HR and NDCG as evaluation indicators,the above two data sets are used to verify that the hybrid recommendation model performs better in ranking accuracy and recommendation accuracy than algorithm models such as FM,Deep FM,and x Deep FM.Finally,referring to the design scheme of the popular recommendation system on the Internet,obtain user and movie information from Movielens-100 k dataset.And a personalized movie recommendation system is built based on the hybrid recommendation algorithm proposed in this paper.And carried on the demand analysis,the database design,the architecture design and the function module design respectively,at the same time designed the function test to verify the personalized recommendation system's main function.