融合深度情感分析和评分矩阵的推荐模型

目前，大多数推荐系统都具有评分数据稀疏性的问题，它会限制模型的有效性。而用户对于某件商品撰写的评论中隐含了很多信息，对评论文本进行情感分析并提取关键的因素来用于模型的学习，可以有效地缓解数据稀疏问题，但仅使用评论数据而忽略了评分数据的主要因素会影响推荐精度。对此，为了进一步提高推荐精度，该文提出一个评论文本和评分矩阵交互(RTRM)的深度模型，该模型能够提取评论文本和评分矩阵的深层次特征，并结合它们进行评分预测；其次，通过使用预训练的Electra模型得到每条评论的隐表达，并结合深度情感分析及注意力机制实现从上下文语义层面对评论文本的分析，解决了短文本的语义难以分析的问题；同时，在融合层模块中，用户(物品)评论和评分矩阵进行交互，最终预测出用户对商品的评分；最后，在6组数据集上，采用均方误差(MSE)进行性能对比实验，实验结果表明该文模型性能优于其他系统，且平均预测误差最大降低了12.821%，该模型适用于向用户推荐精确的物品。

Recommendation Model Combining Deep Sentiment Analysis and Scoring Matrix

Most recommendation systems have a data sparsity problem, which limits the validity of the model that they use. However, the user’s comments on a commodity contain a lot of information. Emotional analysis of the comment text and the extraction of key factors for model learning can effectively alleviate the data sparsity problem, but only the use of comment data and ignore the main factors of the scoring data will affect the recommendation accuracy. To improve further the precision of recommendations, a deep model for the processing of Review Texts and Rating Matrices (RTRM) is proposed. The model extracts deep-level features and combines them to make rating predictions. Then, by using the pre-trained Electra model, the implicit expression of each comment is get, and combining with the deep emotion analysis and attention mechanism, the analysis of the comment text is realized from the context semantic level. It solves the problem that it is difficult to analyze the semantics of short text; User (item) reviews interact with a rating matrix to predict the user’s rating of a product in the fusion layer module. Finally, the Mean Square Error (MSE) is used to perform performance comparison experiments on 6 sets of data sets. Experimental results show that the performance of the proposed model outperforms significantly other systems on a variety of datasets, and the average prediction error is reduced by a maximum of 12.821%, the model is suitable for recommending accurate items to users.