Research on Literature Recommendation Methods Based on TextCNN and PU-Learning
This paper aims to apply existing machine learning research to the practical work of library literature recommendation,in order to make full use of electronic re-sources.Due to the difficulty in obtaining users'explicit ratings on literature re-sources,the literature browsed and downloaded by users is treated as the positive,and literature without user interaction is treated as unlabeled.The recommendation probability of candidate literature is predicted through TextCNN classification model combined with PU-Learning algorithm.Practice has proved that this method has high accuracy and can play a role in the actual application of library literature recommendation.
Convolutional neural networkElectronic literature recommendationPU-LearningText classification
NETL
NSTL
万方数据
维普
