基于异质信息网络的模糊推荐算法

随着互联网信息的爆炸式增长,推荐系统扮演着越来越重要的角色。为了解决传统推荐系统存在的信息稀疏问题,并且合理表达用户的偏好,提出基于异质信息网络的模糊推荐算法(HFR)。HFR方法构建三角模糊评分模型将用户离散的评分信息模糊化,此外,还加入了项目的属性信息并使用元路径表示;在此基础上充分利用多源信息,提出了一种新的相似性度量,并预测评分获得最终的推荐结果。实验结果表明,HFR方法有效解决了信息稀疏问题,提高了推荐质量。

A fuzzy recommendation method based on heterogeneous information network

With the explosive growth of Internet information, the recommendation system plays an increasingly important role. In order to solve the problem of sparse information in the traditional recommendation system and to reasonably express the user’s preference, a fuzzy recommendation algorithm (HFR) based on heterogeneous information network is proposed. The HFR algorithm constructs a triangular fuzzy scoring model to fuzzify the user’s discrete scoring information. In addition, it also adds the attribute information of the project and uses the meta-path representation. Based on this, the multi-source information is fully utilized and a new similarity measure is proposed. The score is predicted to get the final recommendation result. The experimental results show that the HFR algorithm effectively solves the problem of sparse information and improves the recommendation quality.