Application of Sensor and Fuzzy Clustering Algorithm in Hybrid Recommender System

Abstract

In order to solve the problem of topic drift and topic enlargement in hybrid recommendation system, a possibility $C$ clustering algorithm based on fuzzy clustering, namely, IPCM (improved possible clustering method) algorithm, is proposed. This method improves the initial value sensitivity of PCM algorithm and introduces the user interest model into the initial matrix, so that the results obtained by the convergence of IPCM algorithm are closer to the recommended topics required by users. The recommended technology algorithm is also fused by learning from each other to form a fusion recommendation algorithm. The fusion recommendation algorithm and IPCM algorithm are applied to the result sorting, and the accuracy of the applied results is compared with that of the traditional PageRank algorithm, so as to judge the accuracy of the algorithm. The feasibility and superiority of the algorithm are verified by experiments. The experimental results show that IPCM algorithm can speed up the search for useful information and reduce the search time. Moreover, when the query range is reduced, the accuracy of the algorithm is higher than that of the traditional algorithm, which can be improved by 10% ~30%. Conclusion. This method can effectively make up for the problems of topic drift and topic enlargement in the recommendation system, with faster speed and higher accuracy.