Recently,　product　images　have　been　gaining　the　attention　of　recommender　system　researchers　in　the　field　of　visual　recommendation.　This　is　because　the　visual　appearance　of　products　has　a　significant　impact　on　consumers＇　decisions.　Extensive　studies　have　been　done　to　integrate　the　features　extracted　by　convolutional　neural　networks　directly　into　recommendations.　This　improves　the　performance　of　recommender　systems.　Style　features,　an　important　type　of　features,　are　rarely　considered.　Style　features　play　a　vital　role　in　the　visual　recommendation　as　a　user＇s　decision　depends　largely　on　whether　the　product　fits　his/her　style.　However,　the　representation　of　the　conventional　image　features　fails　in　capturing　the　styles　of　a　product.　To　bridge　this　gap,　we　propose　introducing　style　feature　modeling,　which　is　highly　relevant　with　user　preference,　into　the　visual　recommendation　model.　Furthermore,　we　propose　incorporating　the　style　features　into　collaborative　learning　to　create　awareness　pertaining　to　the　preferences　of　users.　The　experiments　conducted　on　two　public　implicit　feedback　datasets　demonstrate　the　effectiveness　of　our　approach　for　the　visual　recommendation.