双通道深度主题特征提取的文章推荐模型

针对传统的文章推荐方法存在的冷启动、用户反馈稀疏以及相似度衡量准确性欠佳等问题,本文提出了融合主题模型和预训练模型BERT的文章相似度计算模型(contextualized topic BERT, ctBERT).给定查询,该算法会计算查询与相关文章之间的相似度分数,文章经过预处理分别输入独立的子模块进行特征抽取并计算相似度得分,相似度得分与支撑集的个性化得分相结合以获得最终分数,该方法将单样本学习整合进推荐框架中,进一步取得了显著的改进.本文在3个不同的数据集上的实验结果表明,所提出方法在3个数据集上的NDCG标准均有提升,例如在Aminer数据集上NDCG@3和NDCG@5标准比对比方法分别提高了6.1%和7.2%,验证了该方法的有效性.

Article Recommendation Model with Two-channel Deep Topic Feature Extraction

For the cold start, sparse user feedback, and poor accuracy of similarity measurement in traditional article recommendation methods, this study proposes contextualized topic BERT (ctBERT), an article similarity calculation method that combines BERT with the topic model. The algorithm calculates the similarity scores between the given query and the related articles. The preprocessed articles are input into separate sub-modules for feature extraction and similarity score calculation. The similarity score is combined with the personalization score of the support set to obtain the final score. The algorithm is further improved by integrating single-sample learning into the recommendation framework. The experimental results from three different datasets show that the proposed method improves the NDCG criteria on all three datasets. For example, the NDCG@3 and NDCG@5 criteria improve by 6.1% and 7.2% respectively compared with other methods on the Aminer dataset, which validates the effectiveness of the method.