摘要
为了有效地解决协同过滤算法中新项目难以推荐的问题,文中提出了一种对项目矩阵进行划分的方法。其基本思想是,首先利用分类树算法划分项目矩阵并计算项目间的相似度,在此基础上缩小近邻搜索的范围和需要预测的资源数目。通过用户对已有项目的评分排列顺序和项目间相似性预测用户对新项目的评分。实验结果表明:基于项目矩阵划分的协同过滤算法有效地解决新项目推荐困难的问题,显示出了比传统推荐算法更好的推荐质量和扩展性。
To efficiently resolve the problem that the new item is difficult to recommend in collaborative filtering algorithm, In this paper we propose a new method based item matrix partition. The essential idea was that the item matrix can be partitioned by using classification tree algorithm and get low-dimensional matrices. This method predicts new item rating based on item rating that users have rated and item similarity. Compared traditional collaborative filtering method, the experimental results show that our approach can find a solution to the problem of new item recommendation effectively.
出处
《微计算机信息》
北大核心
2005年第11X期186-187,100,共3页
Control & Automation
基金
陕西省自然科学基金(98X11)
陕西省教育厅重点科研计划项目(00JK015)
关键词
协同过滤
项目相似性
矩阵划分
个性化推荐
分类树
平均绝对偏差
Collaborative filtering
item similarity
Matrix partition
Personalized recommendation
Classification tree
MAE
