Abstract We usually use hit rate to measure the performance of item recommendation algorithms. In addition to hit rate, we consider additional two important factors which are ignored by most previous works. First, we consider whether users are satisfied with the recommended items. It is possible that a user has bought an item but dislikes it. Hence high hit rate may not reflect high customer satisfaction. Second, we consider whether the website retailers are satisfied with the recommendation results. If a customer is interested in two products and wants to buy one of them, it may be better to suggest the item which can help bring more profit. Therefore, a good recommendation algorithm should not only consider improving hit rate but also consider optimizing user satisfaction and website revenue. In this paper, we propose two algorithms for the above purposes and design two modified hit rate based metrics to measure them. Experimental results on 10 real-world datasets show that our methods can not only achieve better hit rate, but also improve user satisfaction and website revenue comparing with the state-of-the-art models.