Effect of recent popularity on heat-conduction based recommendation models

Accuracy and diversity are two important measures in evaluating the performance of recommender systems. It has been demonstrated that the recommendation model inspired by the heat conduction process has high diversity yet low accuracy. Many variants have been introduced to improve the accuracy while keeping high diversity, most of which regard the current node-degree of an item as its popularity. However in this way, a few outdated items of large degree may be recommended to an enormous number of users. In this paper, we take the recent popularity (recently increased item degrees) into account in the heat-conduction based methods, and propose accordingly the improved recommendation models. Experimental results on two benchmark data sets show that the accuracy can be largely improved while keeping the high diversity compared with the original models.