Design And Realization Of The Recommended Model Based On The Slope One Improved Algorithm

Since1990s, human society enters the age of information network almost everywhere with the rapid development of Internet. Knowledge and information are spread on the Internet. People can get mass of data from the Internet. It enormously promoted the social progress and development. However, people find that it become more and more difficult to find the information they are interested in from mass of data. In this situation, the recommend system arises at the historic moment. The task of recommend system is contact information between consumers and information producers. On the one hand recommend system help information consumers to find useful information, on the other hand recommend system help information producers production information, convenient to show the information of interest before consumer information, so as to realize consumer information and information producers both win-win situation. Recommend system according to the number of the service object is divided into personalized recommendation and group recommendation. Just as its name implies, personalized recommendation is recommended for individuals to provide services, the group recommendation is recommended for group to provide services. And personalized recommendation and group recommendation core parts is recommendation algorithm.The thesis analyzes the current main recommendation algorithm, this paper introduces their principle, points out the deficiency of them. In order to obtain better recommendation effect, the thesis deeply analyzes the Slope One algorithm, and in the light of the shortage of the algorithm, this paper puts forward the following three improvement measures.Firstly, according to the Slope One algorithm without considering the user similarity and use traditional similarity measurement method can increase the complexity of the algorithm to improve the defect, this paper introduces a new similarity measure method, and put forward Slope One algorithm based on the users’ partial similarity.Secondly, Slope One algorithm in the prediction of the scoring process will use and target project completely different resources to produce prediction score, which will cause greater error. In order to solve this problem, this paper introduce k-means clustering algorithm to improve Slope One algorithm. First of all through the k-means clustering algorithm to cluster project points, and then put forward Slope One improved algorithm based on k-means clustering. Thirdly, the recommend effect of Slope One algorithm under the condition of spare data is not good. Multi-dimensional sparse matrix usually reduces the data set of sparse degree by dimension reduction. Matrix decomposition method is matrix dimension reduction process commonly used methods, and matrix decomposition method also has the function of noise filter data. It can not only effectively to data generalization, and played a reduced-order diminish purpose. This paper introduced the matrix decomposition method of singular value decomposition to improve Slope One algorithm, and Slope One algorithm based on singular value decomposition is proposed.The paper is validated through the experiment three improved Slope One algorithm recommendation effect. The experimental results show that compared with the.original Slope One algorithm and the traditional collaborative filtering algorithm, this paper proposes the Slope One improved algorithm can effectively improve the accuracy of the recommend system.