The Journal of China Universities of Posts and Telecommunications ›› 2021, Vol. 28 ›› Issue (5): 82-90.doi: 10.19682/j.cnki.1005-8885.2021.0026

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News recommendation based on time factor and word embedding


  • Received:2020-11-25 Revised:2021-02-26 Online:2021-10-31 Published:2021-10-29
  • Supported by:
    National Defense Basic Scientific Research program of China


Existing algorithms of news recommendations lack in depth analysis of news texts and timeliness. To address these issues, an algorithm for news recommendations based on time factor and word embedding ( TFWE) was proposed to improve the interpretability and precision of news recommendations. First, TFWE used term frequency- inverse document frequency ( TF-IDF ) to extract news feature words and used the bidirectional encoder representations from transformers ( BERT ) pre-training model to convert the feature words into vector representations. By calculating the distance between the vectors, TFWE analyzed the semantic similarity to construct a user interest model. Second, considering the timeliness of news, a method of calculating news popularity by integrating time factors into the similarity calculation was proposed. Finally, TFWE combined the similarity of news content with the similarity of collaborative filtering ( CF) and recommended some news with higher rankings to users. In addition, results of the experiments on real dataset showed that TFWE significantly improved precision, recall, and F1 score compared to the classic hybrid recommendation algorithm.

Key words:

news recommendation, time factor, word embedding, user interest model

CLC Number: