关键词: 大数据, 随机森林, 情境感知, 基于位置服务, 推荐系统

Abstract:

Context-aware recommender system improves recommendation accuracy by adding context information, which is widely used in practical applications. However, the traditional context-aware recommendation method gives the same weight to the contextual factors, ignoring the differences of users’ preference items in different contexts, and the different influences of the contextual factors in the recommendation process. This paper presents a context-aware recommendation method based on multi-subdomain random forest algorithm. The method first improves the random forest algorithm by randomly selecting features from multiple feature subspaces which are classified by the importance of features. In addition, it uses the improved random forest algorithm to decompose and reduce the dimensions of context features of users, items and contexts. Finally, it uses the collaborative filtering recommendation algorithm to carry out personalized recommendations for cold chain logistics distribution. The simulation experiments of two data sets of LDOS-CoMoDa and Cycle Share show that the average absolute error of this method is reduced by nearly 10% compared with the traditional methods, which effectively improves the prediction accuracy of the recommender system and provides reference for the application of context-aware recommendation.

Key words: big data, random forest, context-aware, location-based service, recommender system