This paper proposes a novel algorithm named itembased clustering recommendation algorithm (IBCRA) for reducing the poor recommendation quality due to the data sparsity and high dimension. Specifically, on the basis of high-dimensions data clustering algorithms, the IBCRA uses the rating data sparse difference and item categories in the rating dataset to construct a measuring formula for calculating dataset difference, where the formula is used for item clustering in user-item rating array. Then the IBCRA calculates item similarity and searches for k-nearest neighbors of target item based on the outcome of item clustering. Finally it predicts the ratings for those no rating items in dataset and so generates recommendations. The experimental results show, in perspective of the accuracy and speed of convergence, the IBCRA has improved the recommendation quality in collaborative filtering recommendation. Therefore, it can be used to recommend the products in ecommerce recommending systems.