Because existing passenger-finding algorithms do not consider taxi's spatio-temporal context, a collaborative filtering recommendation algorithm of taxi passenger-finding based on spatio-temporal context was proposed. The proposed algorithm mapped potential passenger locations to space network, and introduced time delay factor to similarity measure to get the neighbor set which was similar to a target taxi's driving behavior. Based on location context, the proposed algorithm chose the target taxi's most interest potential passenger location from similar neighbor set. The experimental results on Fuzhou taxi trajectory data show that the proposed algorithm can get the best recommendation result when the time delay factor is 0.7. Meanwhile, compared to the traditional collaborative filtering recommendation algorithms, the proposed algorithm obtains better recommendation result under the neighbor sets with different size, which means the proposed algorithm is more accurate than the traditional collaborative filtering algorithms.