The　problem　of　different　contextual　information　to　influence　the　user-item-context　interactions　at　varying　degrees　in　context-aware　recommender　systems　is　addressed.　To　improve　the　performance　accuracy,　we　develop　a　novel　attribute　reduction　algorithm　in　order　to　effectively　extract　the　core　contextual　information　using　rough　set.　We　combine　collaborative　filtering　with　contextual　information　significance　to　generate　more　accurate　predictions.　We　experimentally　evaluate　our　approach　on　UCI　machine　learning　repository　and　two　real　world　data　sets.　Experimental　results　demonstrate　that　our　proposed　Approach　outperforms　existing　state-of-theart　context-aware　recommendation　methods.