The　current　Web　manifests　the　problem　of　information　overload　due　to　the　success　of　the　Web　2.0　paradigm　in　which　users　can　provide　new　contents　quickly.　To　help　users　find　the　most　valuable　information,　a　recommendation　system　is　designed　in　which　we　use　Euclidean　formula　to　calculate　the　distance　and　Cosine　formula　to　calculate　the　angle　to　distinguish　between　different　kinds　of　users.　Thus,　similar　users　will　receive　related　items.　In　the　beginning,　we　will　face　some　problems　such　as　the　cold　start　due　to　a　small　amount　of　data.　We　will　advance　some　theories　to　solve　the　problem.　With　the　proposed　method,　we　can　improve　the　quality　of　recommendation　so　that　users　can　find　the　most　valuable　information.　©　2014　IEEE.