摘要
协同过滤技术是推荐系统中应用最为广泛的算法,其面临着数据稀疏性问题、冷启动、规模可扩展性等问题。工作体现在两点:一是在基于项的协同过滤模型中,改进了项间的相似度计算方法,相比调整余弦方法仅考虑一个要素,包含了三个要素:两项的具有共同用户的评分、共同评分用户数量、非共同评分用户数量;二是组合基于用户、基于项和基于奇异值分解的协同过滤推荐,通过多模型组合提高推荐性能。实验结果表明在基于项过滤中MAE指标上提高了4.30%。进一步,加权的组合多种模型方法比基于项方法提高了1.26%。
Collaborative filtering is one of the typical personal recommendations,but some difficulties exist,such as the data sparse problem,cold starting problem,scaling expanding problem.Two respects of work are done：on one hand,an improved similarity measure is presented to overcome the data sparse problem,on the other hand,the SVD based special user erasing method is presented to overcome the noise-sample problem.The experiments show that the improved method increases 4.30% in terms of MAE,and the combined model,which adopts the weighted average method,further increases 1.26%.
出处
《计算机工程与应用》
CSCD
2012年第21期21-25,30,共6页
Computer Engineering and Applications
基金
国家自然科学基金(No.70801022)
中央高校基本科研业务费专项资金(No.HIT.NSRIF.2010083)
中国博士后基金(No.20090450973)
黑龙江省博士后基金(No.LBH-Z09144)
黑龙江省教育厅科学技术研究项目(No.12511435)
关键词
个性化推荐
协同过滤
数据稀疏问题
组合推荐
personal recommendation
collaborative filtering
data sparse problem
combined recommendation
