基于项目属性偏好的协同过滤算法  

Collaborative Filtering Algorithm Based on Item Attribute Preference

作　　者：朱明[1] 魏慧琴[1] 

机构地区：[1]北京交通大学计算机与信息技术学院,北京100044

出　　处：《计算机与现代化》2017年第4期23-26,共4页Computer and Modernization

基　　金：国家自然科学基金"青年科学基金"资助项目(K11A800020)

摘　　要：针对传统的协同过滤算法存在的数据稀疏性问题,提出一种基于项目属性偏好的协同过滤算法(CFBIAP)。该算法利用项目属性和评分计算基于项目属性偏好的用户相似性,并且与基于评分矩阵的相似性线性拟合得到用户相似性,一定程度上减小了传统的仅依据评分矩阵计算用户相似性所产生的误差。在MovieLens数据集上的实验表明,该算法推荐的质量和效果均优于传统的协同过滤算法,有效解决了矩阵稀疏性问题。To tackle the data sparse problem of the traditional collaborative filtering algorithm, this paper proposes a collaborative filtering algorithm based on item attribute preference （CFBIAP）. This algorithm calculates user similarity based on item attribute preference by using the item attributes and scores. Meanwhile it makes linear fitting with the similarity based on score matrix to get the user similarity. Up to a point, it decreases the error merely by score matrix partly. Experiments on MovieLens dataset show that the recommendation is better than traditional collaborative filtering algorithm both in quality and effect. The algorithm solves the data sparse problem effectively.

关 键 词：协同过滤 推荐 项目属性 相似性 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]