一种基于可拓学的电子商务内容推荐算法研究

Research on E-commerce Content-based Recommendation Based on Extension Theory

为研究电子商务内容推荐算法,论文从电子商务推荐系统中的瓶颈问题——相似度计算出发,借助可拓学的方法进行分析。论文将可拓学中基元的表示方法引入推荐算法中,并采用关联函数和可拓距的思想,实现了产品的表示和相似度计算,进而得到了一种新的内容推荐算法。然后通过实例分析对算法进行验证,确认了方法的可靠性和有效性。论文的研究将可拓学思想引入到推荐算法中,这对可拓思想的应用以及推荐算法的实现都起到了有益的作用。

In order to study e-commerce content recommendation algorithm, the research begins with the bottleneck problem of e-commerce recommendation system the calculation of similarity, and studies it with the help of extenies method. The method introduces the representation of the basic element in the extenics into the recommendation algorithm. It uses the idea of correlation function and the extension distance to realize the representation of product and the calculation of similarity, and then gets a new content recommendation algorithm. After that, the research uses the data of the actual cause to validate the algorithm, and confirms the reliability and validity of the method. The extension theory is introduced into the recommendation algorithm in the study of this research, which plays an important role in the application of extension theory and the implementation of the recommendation algorithm.