摘要
针对传统协同过滤算法中面临稀疏项目评分矩阵计算耗时不准确、同等对待不同时间段用户的项目评分这些影响推荐精度的问题,提出了基于项目聚类和评分的时间加权协同过滤推荐算法(TCF)。该算法将项目评分与项目属性特征综合相似度高的聚到一个类别里,能有效解决数据稀疏性问题,降低生成最近邻居集合时间。引入时间加权函数赋予项目评分按时间递减的权重,根据加权后的评分寻找目标用户的最近邻居集合。实验从平均绝对误差、平均排序分和命中率三个指标来表明改进算法能有效提高推荐的准确性。
The calculation of traditional collaborative filtering algorithm is time-consuming and not accurate when faced large data of user project set, same treatment to user's item scores in different period also affect the accuracy. Therefore this paper presented the collaborative filtering algorithm based on item clustering and time-weighted item score (TCF). Firstly ,this algorithm took the high comprehensive similarity with item score and characteristics into a category could effectively solve the problem of data sparsity and reduced the time to generate a set of the nearest neighbors. Secondly, it proposed time-weighted function gave the item time descending weights, and then used the weighted score to find the target user's nearest neighbors set. Experiment from three indicators:the mean absolute error, mean sorting point and hit rate all show that improved algorithm can effectively improve the recommendation accuracy.
出处
《计算机应用研究》
CSCD
北大核心
2015年第7期1966-1969,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61073021
61272438
60970012
61202376)
上海市科委资助项目(12511502704
11511500102
10DZ1200200)
上海市教委科研创新基金资助项目(13ZZ112
13YZ075)
关键词
协同过滤
同等对待
项目聚类
时间加权
最近邻居
准确性
collaborative filtering
same treatment
item clustering
time-weighted
nearest neighbors
accuracy