【摘 要】
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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 pre
【机 构】
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School of Automation,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;Enginee
论文部分内容阅读
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.
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