The　collaborative　filtering　(CF)　technique　has　been　widely　used　recently　in　recommendation　systems.　It　needs　historical　data　to　give　predictions.　However,　the　data　sparsity　problem　still　exists.　We　propose　a　new　item-based　restricted　Boltzmann　machine　(RBM)　approach　for　CF　and　use　the　deep　multilayer　RBM　network　structure,　which　alleviates　the　data　sparsity　problem　and　has　excellent　ability　to　extract　features.　Each　item　is　treated　as　a　single　RBM,　and　different　items　share　the　same　weights　and　biases.　The　parameters　are　learned　layer　by　layer　in　the　deep　network.　The　batch　gradient　descent　algorithm　with　minibatch　is　used　to　increase　the　convergence　speed.　The　new　feature　vector　discovered　by　the　multilayer　RBM　network　structure　is　very　effective　in　predicting　a　rating　and　achieves　a　better　result.　Experimental　results　on　the　data　set　of　MovieLens　show　that　the　item-based　multilayer　RBM　approach　achieves　the　best　performance,　with　a　mean　absolute　error　of　0.6424　and　a　root-mean-square　error　of　0.7843.