A novel scheme for item-based recommendation is proposed in this paper. In our framework, the items are described by an undirected weighted graph G = (V, E). V is the node set which is identical to the item set, and E is the edge set. Associate with each edge e_ij \in E is a weight w_ij \geqslant 0, which represents similarity between items i and j. Without the loss of generality, we assume that any user?s ratings to the items should be sufficiently smooth with respect to the intrinsic structure of the items, i.e., a user should give similar ratings to similar items. A simple algorithm is presented to achieve such a "smooth" solution. Encouraging experimental results are provided to show the effectiveness of our method.