Design And Implementation Of Collaborative Filtering Recommendation System Based On Improved Fuzzy C-Means

As mobile Internet,cloud computing,Internet of Things and other technologies are maturing,the amount of data explodes exponentially.Both consumers of information and producers of information have encountered great challenges,and the recommendation system has become an important means to deal with information overload..The recommendation system has been successfully applied to various fields such as e-commerce,movie and video websites,online music,and social networking platforms.In the recommendation system,the collaborative filtering recommendation algorithm is widely used and has the most far-reaching impact,but there are sparseness problems,cold start problems,and scalability problems.Based on the analysis of recommendation algorithm,this thesis proposes an improved FCM collaborative filtering recommendation method to achieve data aggregation,solve data sparsity,and improve recommendation accuracy.The proposed improved algorithm is applied to the hybrid recommendation algorithm based on FCM.First,the data is preprocessed to obtain the target score data,and then the user preference feature matrix is combined with the user score and project characteristics to effectively alleviate the sparsity of the data;second,the data is normalized to reduce the data difference;then,based on fuzzy C-means clustering The algorithm analyzes user clustering,and uses genetic clustering algorithm to optimize the initial clustering center to prevent the fuzzy C-means clustering algorithm from appearing local optimal solutions and enhance the user's clustering effect;then,a hybrid method is used to calculate users Similarity,weighting and summing the similarity of user preference information and the similarity of the user item rating matrix to calculate the user similarity,and then obtaining multiple nearest neighbor matrices;finally,considering the impact of time factors on user interest,improving user interest changes Influence the recommendation effect and improve the quality of the recommendation.Finally,a hybrid recommendation algorithm based on FCM is proposed and applied to the collaborative filtering recommendation system.Experimental results show that the improved algorithm improves the degree of data aggregation and eases the data sparsity.The accuracy and recall rate prove that the improved hybrid recommendation algorithm improves the accuracy of recommendation.