dpSmart: A Flexible Group Based Recommendation Framework for Digital Repository Systems

• Main Authors: Guan, Boyuan, Hu, Liting, Liu, Pinchao, Xu, Hailu, Fu, Zhaohui, Wang, Qingyang
• Format: Conference Proceeding
• Language: English
• Subjects: automatic log mining; automatic user recognition; group based recommender; stereotyping recommendation; subspace clustering
• ISSN: 2642-7273
• DOI: 10.1109/BigDataCongress.2019.00028

Digital Repository Systems have been used in most modern digital library platforms. Even so, Digital Repository Systems often suffer from problems such as low discoverability, poor usability, and high drop-off visit rates. With these problems, the majority of the content in the digital library platforms may not be exposed to end users, while at the same time, users are desperately looking for something which may not be returned from the platforms. The recommendation systems for digital libraries were proposed to solve these problems. However, most recommendation systems have been implemented by directly adopting one specific type of recommender like Collaborative-Filtering (CF), Content-Based Filtering (CBF), Stereotyping, or hybrid recommenders. As such, they are either (1) not able to accommodate the variation of the user groups, (2) require too much labor, or (3) require intensive computational complexity. In this paper, we design and implement a new recommendation system framework for Digital Repository Systems, named dpSmart, which allows multiple recommenders to work collaboratively on the same platform. In the proposed system, a user-group based recommendation strategy is applied to accommodate the requirements from the different types of users. A user recognition model is built, which can avoid the intensive labor of the stereotyping recommender. We implement the system prototype as a sub-system of the FIU library site (http://dpanther.fiu.edu) and evaluate it on January 2019 and February 2019. During this time, the Page Views have increased from 8,502 to 10,916 and 10,942 to 12,314 respectively, compared to 2018, demonstrating the effectiveness of our proposed system.