作　　者： ; ;

机构地区： 江苏大学计算机科学与通信工程学院

出　　处： 《计算机工程与应用》 2011年第8期72-75,共4页

摘　　要： 协同过滤是迄今为止个性化推荐系统中采用最广泛最成功的推荐技术,但现有方法是将用户不同时间的兴趣等同考虑,时效性不足,而且推荐精度也有待进一步提高。鉴于此提出一种改进的协同过滤算法,针对用户近邻计算和项目评分的预测两个关键步骤,提出基于项目相关性的用户相似性计算方法,以便邻居用户更准确,同时在预测评分的过程中增加时间权限,使得接近采集时间的点击兴趣在推荐过程中具有更大权值。实验结果表明,该算法在提高了推荐精度的同时实现了实时推荐。 Collaborative filtering is the most widely used and the most successful technology in the personalized recommendation system so far.However,existing collaborative filtering algorithms have taken the user’s interests in different time into equal consideration,which leads to the lack of effectiveness in the given period of time.At the same time,they have been suffering from low recommendation accuracy.Based on two crucial steps：Computing the user’s nearest neighbor and predicting item ratings,this paper proposes an improved collaborative filtering algorithm,which computes user similarity based on the relation between items and adds time weight for computing item ratings,to get more appropriate neighbors and make the click interests approaching the gathering time have bigger weight in recommendation process.Experimental results show that the improved algorithm can provide up to date recommendations and give better prediction in accuracy.

关 键 词： 个性化推荐系统 协同过滤 基于项目的用户相似性 时间权值

领　　域： [自动化与计算机技术] [自动化与计算机技术]