Frontiers of Data and Computing ›› 2021, Vol. 3 ›› Issue (2): 112-119.doi: 10.11871/jfdc.issn.2096-742X.2021.02.013

• Technology and Applicaton • Previous Articles     Next Articles

Research on Resource Recommendation Technology of Scientific Research Information Portal

LI Yan1,2(),CHEN Yuanping1,*()   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-11-15 Online:2021-04-20 Published:2021-05-18
  • Contact: CHEN Yuanping E-mail:liyan@cnic.cn;ypchen@cnic.cn

Abstract:

[Application Background] As the entrance for scientific researchers to obtain resource services, the scientific research information portal has become a workbench for scientific researchers, management decision makers, students and other users. It is used in scientific research activities, scientific research management, education and training, and scientific communication. Each business area plays an important role. [Objective] To address the issue of unreasonable information resources allocation in scientific research information portals, design of recommendation technology suitable for scientific research information portals to improve the push efficiency of information resources is of great significance to scientific researchers. [Methods] This paper proposes a hybrid recommendation algorithm. For new users who use the system for the first time, based on user attributes, neighbor users can be found through K-means clustering to calculate prediction scores. For old users, the problem of lacking negative feedback in implicit feedback is firstly solved by calculating the similarity between users and resources, and then matrix factorization is used to calculate the predicted score. Finally, the prediction scores of the two algorithms can be linearly combined to obtain the final prediction score. The algorithm not only employs the wisdom of the group but also embodies personalization. [Results] Based on user behavior data by embedding code on the real scientific research information portal website, the comparative experiment proves that the proposed recommendation method can solve the cold start problem while ensuring high recommendation accuracy.

Key words: recommendation system, research information portal, user clustering, implicit feedback