The Recommendation Algorithm Based On Clustering Technology Research

Recommendation System is a tool for helping users to evaluate the content he didnot discover; it is also a tool to overcome the information overload. The research onRecommendation System is both of great social significance and significant economicvalue. Since the nineties of the last century, Recommendation System being proposed asan independent subject, related research covers data mining, artificial intelligence,human-computer interaction, user behavior and other disciplines. Targeted on someproblems in the practical applications, data mining researchers give out a number ofsolutions and improvement strategies from the perspective of algorithms. These worksmake that Recommendation System has been successfully used in various commercialsystems.Although personalized recommendation technology has been widely used in theInternet, there are still some problems which should be solved, such as data sparsenessproblem, scalability of algorithms and "cold start" problem. The thesis aims tounderstand the various elements of the recommendation systems and solve existingproblems using approaches such as cluster analysis and complex networks, The mainresults are as follows:1. The thesis proposes a recommention algorithm based on item-based clustering.The core of the algorithm aims to deal with the original information andprovide a more accurate recommendation for real users by transformingbehavior model into interest model through the introduction of users’belongingcoefficient, while retain the advantages of other cluster-based recommendationalgorithms in solving data sparsity problem. Many experiments with couple ofdifferent kinds of data sets are carried out and we also use offline simulation toevaluate the accuracy of the algorithm, results show that the predictionaccuracy of this algorithm has a greater enhancement compared with theorigional algorithm.2. The thesis proposes a multi-B2C crossing ranking recommendation algorithm.The algorithm makes use of users’ accessing information in a number of different categories of E-commerce sites to recommend, after we analysis themain tasks and problems of existing recommendation systems in E-commerceapplications, the recommendation is of accuracy and personality by offlinesimulation. Even using only the data of one site to recommend, its accuracycan be much better than random recommendation, while maintaining randomrecommendation’s diversity and novelty. These are to ensure that the algorithmhas a good user experience. As more known information is taken into account,the accuracy of this algorithm has a greater enhancement. This part of workprovides a brand new object of data and perspective for commercial applicationof recommendation systems.