摘要
针对传统的基于用户的协同过滤推荐算法存在用户兴趣偏好模型过于粗糙和邻居集不够准确等问题,提出了一种新的协同过滤推荐算法,命名为基于用户间引力的协同过滤推荐算法。该算法认为用户使用的社会标签可以反映用户的喜好类型及喜好程度,利用社会标签构建用户喜好物体模型,并计算它们之间的万有引力,把万有引力的大小作为用户相似度的度量,在此基础上获得目标用户的邻居用户和评分预测,把获得预测评分高的若干项目推荐给用户。实验结果说明算法可以获得比其他算法较优的推荐性能。
There are some shortcomings in user-based collaborative filtering, this paper proposed a new collaborative filtering recommendation algorithms, named user＇ s gravitation based collaborative filtering （UGBCF） recommendation algorithm, it used a new method of similarity measure to improve the user-based collaborative recommendation algorithms. This paper thought that the social tags used by user can reflect user＇ s preference and how much the preference, so it used those social tags to build user＇ s preference object model. It computed the gravitation between preference objects, viewed the gravitation as the similarity of users. According to the similarity, the neighbor user of the target user could be gotten, and the prediction score of his unseleeted items could be calculated by aggregated the neighbor users＇ score. The results of experiment show that UGBCF can provide better recommendation quality than other collaborative filtering recommendation.
出处
《计算机应用研究》
CSCD
北大核心
2016年第11期3329-3333,共5页
Application Research of Computers
关键词
推荐算法
协同过滤推荐
万有引力定律
社会标签
recommendation algorithms
collaborative filtering
universal law of gravitation
social tag
