A Package Recommendation Model Based on Credit and Time

Abstract

Nowadays, reading has become increasingly important for people who want acquire knowledge for a better life all around the world. As a result, book recommendation systems are useful to these readers. However, many readers are confused about how to choose right books for themselves. In this paper we propose a model to recommend a set of book packages to readers, where each package contains different categories of books. Our Packages consider users’ credit, the popularity of books, intra-package diversity, and user preference which may change over time. Experimental results suggest that our method presents improvement on recommendation accuracy and diversity.