Abstract: This paper improves traditional collaborative filtering algorithm, proposes a new user profile based on concept hierarchy tree, which can make recommender systems still work even when users have no common rating items. In the process of similarity calculation and recommendation formation, it also uses concept hierarchy thought to generate recommendation lists by different categories, avoiding recommendation lack of diversity. Experimental results on MovieLens dataset show that the improved algorithm can provide better prediction in either accuracy or diversity aspect.

Key words: personalized recommendation, collaborative filtering, concept hierarchy tree

摘要： 改进了传统的协同过滤算法，提出了基于概念层次树的用户模型，利用该模型进行协同运算，使系统在用户共同评分项极其稀疏时也能产生推荐。在相似性计算和产生推荐阶段引入了概念分层思想，分别在商品种类上产生推荐，避免了推荐的单一现象。MovieLens数据集实验表明，改进后的算法在推荐质量上有了明显的提高。

关键词: 个性化推荐, 协同过滤, 概念层次树

CLC Number: 

• TP311.1