[Objective] This paper explores a new method combining two basic recommendation algorithms to improve the recommendation accuracy of the personalized recommendation method. [Methods] The trusted neighbors can be obtained by putting forward a calculation method of the project heat to optimize the algorithm of Pearson Correlation Coefficient and establishing the interest model for the current users and its neighbors. [Results] The experiment set in MovieLens 1M movie rating data shows that the hybrid recommendation method proposed in this paper can acquire better recommendation accuracy than the exist two kinds of hybrid recommendation methods. [Limitations] The unique characteristics of the projects need to be selected by different people who may have different opinions to the number of the characteristics and their weight distribution in the interest model. [Conclusions] The hybrid recommendation method proposed in this paper improves the recommendation accuracy of the personalized recommendation.
高虎明, 赵凤跃. 一种融合协同过滤和内容过滤的混合推荐方法[J]. 现代图书情报技术, 2015, 31(6): 20-26.
Gao Huming, Zhao Fengyue. A Hybrid Recommendation Method Combining Collaborative Filtering and Content Filtering. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2015.06.04.
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