基于主题分组与随机游走的 App 推荐算法.

In recent years, the explosive growth of the number of App brings difficulties for user to find a suitable App. There are many limitations of the traditional recommendation approaches when they are applied to App, such as the cold start problem and different choice bias on different types of applications. This paper proposed a personalized recommendation algorithm TGRW, which was based on topic grouping and random walk. Because users have different biases and underling choice factors on the App of different categories, TGRW firstly divided App into categories based on their description information. Then it constructed a triple tuple graphic model consisting of user, App group and App. By applying random walk to calculate the preferential probability of user to each App, it obtained the recommendation list. Extensive experiments on the real data set shows TGRW gains significant improvements on recommendation performances than other methods. [ABSTRACT FROM AUTHOR]