Implementation And Optimization Of Collaborative Filtering Recommendation Algorithm Based On Improved Similarity Measures

Nowadays,due to the rapid growth of Internet information,people are more closely connected with the Internet.People are getting more and more information.A large amount of data enters people's lives,which makes people's lives have a certain convenience,but come along with the problem.In the recommendation system,the algorithm model is a very important core.The collaborative filtering algorithm is a classic algorithm that can still play a huge role today,and many commercial platforms are still in use.But it has some problems: data sparsity,inaccuracy measure,etc.In the face of some of the problems mentioned above,this paper first introduces the current popular recommendation techniques,explains the core principles of collaborative filtering in detail,introduces the idea of cloud model,optimizes the similarity measure in collaborative filtering algorithms,and proposes many A collaborative filtering model is combined to improve the performance of the algorithm,which proves that the proposed algorithm has good performance and improves the recommendation effect.The work of this paper is divided into four parts as follows:1.Describe the current research status of the recommendation system,expound the significance of the research topic,introduce the workflow and algorithm ideas of some common recommendation systems,and have a deep understanding of their advantages and disadvantages.Comparison.2.The core collaborative filtering technology of this paper is deeply analyzed,and its algorithm steps are introduced in detail.The existing problems are also explained,which paves the way for the improvement.3.Introducing the concept of the cloud model,the sparseness of the data can be further optimized.In the process of similarity calculation,the similarity of the basic cloud model is improved,and the shape,familiarity and time factor of the cloud are taking into account.A collaborative filtering recommendation algorithm based on improved cloud model similarity is proposed,and the original collaborative filtering algorithm is compared to verify the advantages of this algorithm.4.Further consider the influence of the combination method on the recommendation effect,introduce multiple combinations,combine the user-based cloud model and the project-based cloud model collaborative filtering,and compare them in a single model to confirm the idea.feasibility.