基于协同过滤和内容过滤的微博话题混合推荐算法
摘要
微博作为社交媒体平台为人们了解信息提供了极大的便利,但同时也使用户每天被海量的信息包围,用户很难迅速地找到自己感兴趣的话题,给用户带来困扰。提出了先进行协同过滤,再进行内容过滤的混合算法,这样尽量可以利用不同算法的优点,避开算法的缺点,基于初步的结果,面向单个用户,进一步采用内容过滤算法,有效提高推荐准确度,从而提高推荐系统的推荐准确度。通过实验表明,提出的混合算法相较于协同过滤推荐算法,在微博话题推荐准确率有显著提升。
引文
[1]B.M.Sarwar,J.A.Konstan,A.Borchers,J.Herlocker B.Miller,and J.Riedl,"Using filtering agents to improve prediction quality in the grouplens research col laborative filtering system,"in Proceedings of the1998 ACM conference on Computer supported cooperative work,1998,pp.345-354:ACM.
[2]Y.Ren,G.Li,J.Zhang,and W.Zhou,"Lazy collaborative filtering for data sets with missing values,"IEEE transactions on cybernetics,vol.43,no.6,pp.1822-1834,2013.
[3]G.-R.Xue et al.,"Scalable collaborative filtering using cluster-based smoothing,"in Proceedings of the28th annual international ACM SIGIR conference on Research and development in information retrieval,2005,pp.114-121:ACM.
[4]Y.Yu,C.Wang,Y.Gao,L.Cao,and X.Chen,"Acoupled clustering approach for items recommendation,"in Pacific-Asia Conference on Knowledge Discovery and Data Mining,2013,pp.365-376:Springer.
[2]Y.Ren,G.Li,J.Zhang,and W.Zhou,"Lazy collaborative filtering for data sets with missing values,"IEEE transactions on cybernetics,vol.43,no.6,pp.1822-1834,2013.
[3]G.-R.Xue et al.,"Scalable collaborative filtering using cluster-based smoothing,"in Proceedings of the28th annual international ACM SIGIR conference on Research and development in information retrieval,2005,pp.114-121:ACM.
[4]Y.Yu,C.Wang,Y.Gao,L.Cao,and X.Chen,"Acoupled clustering approach for items recommendation,"in Pacific-Asia Conference on Knowledge Discovery and Data Mining,2013,pp.365-376:Springer.