Abstract: By analyzing sparsing and cold-start problems of Item-based collaborative filtering algorithm, an Item-based collaborative filtering algorithm using attribute similarity was proposed. This new algorithm revises the original similarity using item attribute, takes synthetically into account the influence of item attribute and user rating. It improved the measure results in the face of data sparity and new item recommendation.