Recommender Systems are helpful to many by filtering the information according to an individual’s preferences. However, the choice of a person may change with time. Keeping track of these changes or predicting the next sequence of items is difficult for any recommender system. Most time-aware recommender systems exploit users’ rating distribution to predict the next possible set of items. But due to the sparsity (users’ preferences) in real-world datasets, these models suffer from low accuracy. To overcome such issues, this study proposes a collaborative filtering model which uses a preference estimation technique that uses user and item feature information along with the ratings. Further, we have used preference relations (instead of ratings) to improve the ranking quality of the recommender system. We have used the ML-1M, NetFlix, and Last.fm datasets for evaluation, the benchmark datasets for recommender systems testing. All these three datasets have sparsity levels above 92%. Experimental results indicate significant improvement (5 to 7%) over the state-of-the-art related baselines.