摘要
针对传统协同过滤算法存在的数据稀疏性和冷启动问题,提出了一种综合项目评分和属性的个性化推荐算法.该算法在衡量项目相似性时,同时考虑用户评分和项目属性特征,并根据评分数据的实际稀疏情况动态调整两者的影响权重;预测评分时,利用用户对项目属性的偏好度来衡量其对未评分邻居项的喜好程度,并产生最终推荐.基于MovieLens数据集进行的实验结果表明,该算法使得最近邻的确定更加准确,系统推荐质量明显改善.
With the problem of data sparsity and cold-start in the traditional collaborative filtering algorithms,a personalized algorithm integrating item rates and attributes is proposed.When measuring the similarity between items,the algorithm takes into account user ratings and item attributes and adjusts the ratio of them for the final similarity according to the spare situation of system ratings.While predicting the score,the user＇s preference on item attributes is adapted to represent current user＇s interest on unrated neighborhood items and produce the final recommendation.Experimental results based on MovieLens data set show that the new algorithm makes neighbor recognition more accurately and improves the system recommended quality significantly.
出处
《微电子学与计算机》
CSCD
北大核心
2011年第9期186-189,共4页
Microelectronics & Computer
基金
国家自然科学基金项目(60673060)
关键词
协同过滤
项目相似性
属性偏好度
冷启动
collaborative filtering
item similarity
attribute preference
cold-start
