Affiliations: [a] Department of Industrial and System Engineering, Zhejiang University, Hangzhou, China
| [b] Center of Engineering Management, Polytechnic Institute, Zhejiang University, Hangzhou, China
| [c] National Institute of Innovation Management, Zhejiang University, Hangzhou, China
| [d] Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China
| [e] School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China
Correspondence:
[*]
Corresponding author. Dr. Fu GU, E-mail: gufu@zju.edu.cn.
Abstract: To enable a quick and accurate access of targeted scientific and technological literature from massive stocks, here a deep content-based collaborative filtering method, namely DeepCCF, for personalized scientific and technological literature resources recommendation was proposed. By combining content-based filtering (CBF) and neural network-based collaborative filtering (NCF), the approach transforms the problem of scientific and technological literature recommendation into a binary classification task. Firstly, the word2vec is used to train the words embedding of the papers’ titles and abstracts. Secondly, an academic literature topic model is built using term frequency–inverse document frequency (TF-IDF) and word embedding. Thirdly, the search and view history and published papers of researchers are utilized to construct the model that portrays the interests of researchers. Deep neural networks (DNNs) are then used to learn the nonlinear and complicated high-order interaction features between users and papers, and the top k recommendation list is generated by predicting the outputs of the model. The experimental results show that our proposed method can quickly and accurately capture the latent relations between the interests of researchers and the topics of paper, and be able to acquire the researchers’ preferences effectively as well. The proposed method has tremendous implications in personalized academic paper recommendation, to propel technological progress.
Keywords: Scientific and technological literature resources, personalized recommendation, deep learning, recommendation algorithm
