基于Vague集的内容推荐算法研究

针对内容推荐系统中商品特征表示的困难，将Vague集方法引入到这一领域，并利用高斯函数和Vague集中未知度的一些性质，将商品的特征用Vague值形象地表示；在此基础上给出了运用Vague集方法进行内容推荐的一般步骤，为推荐系统的设计者提供了一种新的指导思想和方法；最后给出了多个推荐公式，并对各式进行了简单的比较，这将有利于实际推荐工作中酌情选择不同的推荐公式，提高商品推荐质量和推荐精度。

Research on content-based recommendation based on Vague sets

Focused on the trouble of the features representation of merchandise in content-based recommendation system, this paper used the theory of Vague sets, Gaussian function and characteristics of uncertainty to represent features with Vague value. On this basis, gave the general steps of content-based recommendation with Vague sets, in order to get a new idea and method to recommender systems designers. Finally, gave some recommender formula with different features, which would be conducive to the work of the actual recommendation. Select different formula according different condition will improve the quality and accuracy of the recommendation.