A content-based recommender system for computer science publications
By Donghui Wang, Yanchun Liang, Dong Xu, Xiaoyue Feng, Renchu Guan. Knowledge-Based Systems, 2018
332 citations recorded by the source corpus.
Abstract As computer science and information technology are making broad and deep impacts on our daily lives, more and more papers are being submitted to computer science journals and conferences. To help authors decide where they should submit their manuscripts, we present the Content-based Journals & Conferences Recommender System on computer science, as well as its web service at http://www.keaml.cn/prs/ . This system recommends suitable journals or conferences with a priority order based on the abstract of a manuscript. To follow the fast development of computer science and technology, a web crawler is employed to continuously update the training set and the learning model. To achieve interactive online response, we propose an efficient hybrid model based on chi-square feature selection and softmax regression. Our test results show that, the system can achieve an accuracy of 61.37% and suggest the best journals or conferences in about 5 s on average.
Subfields: Computer Vision and Pattern Recognition, Information Systems
View Paper: https://www.semanticscholar.org/paper/5f25bd23f91240d1fff357d0e2cf8b9923b31638
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In Computer Vision and Pattern Recognition
In Information Systems
Citations in the corpus
- A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique (2021, background): “…field [5]–[9], bankruptcy prediction [10]–[16], pattern recognition [17]–[28], image editing [29]–[39], feature reduction [40]–[44], fault diagnosis [45]–[50], face recognition and micro-expression recognition [51]–[57], natural language processing [58], [59] and medical diagnosis [60]–[74].”