Context-aware　recommender　systems,　aiming　to　further　improve　performance　accuracy　and　user　satisfaction　by　fully　utilizing　contextual　information,　have　recently　become　one　of　the　hottest　topics　in　the　domain　of　recommender　systems.　However,　not　all　contextual　information　might　be　relevant　or　useful　for　recommendation　purposes,　and　little　work　has　been　done　on　measuring　how　important　the　contextual　information　for　recommendation.　We　propose　a　heuristic　optimization　algorithm　based　on　rough　set　theory　and　collaborative　filtering　to　using　contextual　information　more　efficiently　for　boosting　recommendation.　Our　approach　involves　three　processes.　First,　significant　attributes　to　represent　contextual　information　are　extracted　and　measured　to　identify　recommended　items　using　rough　set　theory.　Second,　the　user　similarity　is　evaluated　in　a　target　context　consideration.　Third,　collaborative　filtering　is　applied　to　recommend　appropriate　items.　We　perform　an　empirical　comparison　of　three　approaches　on　two　real-world　data　sets.　The　experimental　results　show　that　the　proposed　approach　generates　more　accurate　predictions.