In this work, we solve the ranking-based recommendation task given products with limit life in predicting the preference rank of users on items. We consider the `time-limit' items that can be expired after certain point. We propose to modify the Bayesian Personalize Ranking (BPR) framework to solve the one-class collaborative filtering problem, which directly optimizes the task for ranking. We proposed two enhancements: A time-limit negative sampling technique attempts to solve the ranking predicting task for time-limit items; and a time-aware model attempt to model the temporal effect in its whole life. Experiments on Kiva.org datasets show the merit of the proposed enhancement.