In computerized adaptive testing, overexposure of items in the bank is a serious problem and might result in item compromise. We develop an item selection algorithm that utilizes the entire bank well and reduces the overexposure of items. The algorithm is based on collaborative filtering and selects an item in two stages. In the first stage, a set of candidate items whose expected performance matches the examinee's current performance is selected. In the second stage, an item that is approximately matched to the examinee's observed performance is selected from the candidate set. The expected performance of an examinee on an item is predicted by autoencoders. Experiment results show that the proposed algorithm outperforms existing item selection algorithms in terms of item exposure while incurring only a small loss in measurement precision.