Personalized scientific and technological literature resources recommendation based on deep learning
| Autor: | Fu Gu, Jin Zhang, Yangjian Ji, Jianfeng Guo |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Knowledge management Computer science business.industry Deep learning 05 social sciences General Engineering 02 engineering and technology Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 0509 other social sciences 050904 information & library sciences business |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 41:2981-2996 |
| ISSN: | 1875-8967 1064-1246 |
| DOI: | 10.3233/jifs-210043 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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