Recommender systems are increasingly becoming popular with the enormous choice that the online virtual marts present. Collaborative filtering is one of the most popular techniques to generate recommendation by means of collaboration among multiple information agents. It uses past transactions to gather critical information and then extracts knowledge by means of filtering.
One of the major issues in collaborative filtering is the sparsity problem, wherein the data is sparse in nature and carries only partial information or misses out information totally. Another issue is that in reality, collaborative filtering is characterized by the recency effect wherein recent items tend to speak volumes about user preferences than past data. This concept, sometimes called the drift effect is absent in the traditional collaborative filtering algorithm.