面向用户兴趣密度分布的协同过滤推荐算法

摘要

Aiming to such the problems that sparse data and poor calculation of score similarity result in low quality of recommendation, a collaborative filtering recommendation algorithm based on distribution of user interest density is proposed in the paper. After calculating the similarity of items, classification and entropy are calculated to get finally similarity between two items. Parzen window estimation is applied to get user interest density distribution in total item space. Finally user’s attribute similarity and relative entropy are used to determine nearest neighbour user set. Experimental result shows that the algorithm effectively raises recommendation quality of spare data while avoiding error of filling data.%针对评分数据稀疏和单一评分相似性计算不准确导致推荐质量不高的问题，提出一种面向用户兴趣密度分布的协同过滤推荐算法。在计算项目类别相似度的同时，引入类别的信息熵以确定项目之间距离，在此基础上采用 Parzen 窗估计方法获取用户在整个项目空间上的兴趣密度分布，最后结合用户属性差异性和兴趣密度之间相对熵以确定目标用户的最近邻居用户集。实验结果表明，该算法在避免数据填充所引入误差的同时，有效提升数据稀疏情况下的推荐质量。