The Improved Slope One Algorithm Based On Hadoop Calculation Model

With the rapid development of internet technology, all kinds of information present in front of us. How to find out the information they are interested in the mass of information has become more and more difficult, the phenomenon of "information overload" arises. Personalized recommendation technology is an effective way to solve this problem, and it can recommend products for users by analyzing user’s history information. Collaborative filtering is the most widely used technology. However, with the gradual increase of the number of users and products, existing collaborative filtering algorithm has exposed a lot of problems.The main contents of this paper are after analysis, the Slope One algorithm is not only easy to achieve, but also efficient and effective. However, the Slope One algorithm performs not so well when dealing with personalized recommendation tasks which concern the relationship of users. In order to get a better recommendation results, this paper propose a new collaborative filtering algorithm based on items and the similarity between users. The core idea of the algorithm is a combination of the similarity between users and the Slope One algorithm. First, calculate the average similarity between users. Then, the Slope One algorithm predicts score with the average similarity between users as weights. Finally, the recommendation system gives a list of recommendation for the user. In order to solve the problem of computational complexity, this paper also uses the MapReduce programming model to achieve this new collaborative filtering algorithm in the Hadoop platform.We carry a lot of experiments with the MovieLens data set, and the result proves that the collaborative filtering algorithm based on items and the similarity between users performs more accurate than the Slope One algorithm. Our algorithm is more applicable to today’s social networks.