Category-based dynamic recommendations adaptive to user interest drifts
How to make dynamic recommendations under volatile user interest drifts has been a problem of great interest in modern recommender systems, where challenges lie in accurate and efficient measurement, modeling, and prediction of the user interest drifts. This paper studies a category-based approach to the problem with the key idea that items are aggregated into categories and recommendations are made on each category. In our approach, we use the category-wise rating matrix to measure the changing preferences of users; we design a dynamic adaptive model (DAM) to describe the patterns of interest drifts; and we utilize linear regression to predict the future interests of users in a category-based manner. We have built a category-based dynamic recommender system and tested it with two well-known datasets. Experimental results show that our proposed approach achieves superior performance on category-based rating prediction compared with state-of-the-art dynamic recommendation algorithms.