TP18
随着商家评论网站的快速发展, 推荐系统所带来的效率提升使得评分预测成为近年来新兴研究任务之一. 现有的评分预测方法通常局限于协同过滤算法以及各类神经网络模型, 并没有充分利用目前预训练模型提前学习的丰富的语义知识. 针对此问题, 提出一种基于预训练语言模型的个性化评分预测方法, 其通过分析用户和商家的历史评论, 为用户在消费前提供评分预测作为参考. 该方法首先设计一项预训练任务, 让模型学习捕捉文本中的关键信息. 其次, 通过细粒度情感分析方法对评论文本进行处理, 从而获取评论文本中的属性词. 接下来, 设计一个属性词嵌入层将上述外部领域知识融入模型中. 最后, 采用基于注意力机制的信息融合策略, 将输入文本的全局和局部语义信息进行融合. 实验结果表明, 该方法相较于基准模型, 在两个自动评价指标上均取得显著的提升.
As merchant review websites develop rapidly, the efficiency improvement brought by recommender systems makes rating prediction one of the emerging research tasks in recent years. Existing rating prediction methods are usually limited to collaborative filtering algorithms and various types of neural network models, and do not take full advantage of the rich semantic knowledge learned in advance by the current pre-trained models. To address this problem, this study proposes a personalized rating prediction method based on pre-trained language models. The method analyzes the historical reviews of users and merchants to provide users with rating predictions as a reference before consumption. It first designs a pre-training task for the model to learn to capture key information in the text. Next, the review text is processed by a fine-grained sentiment analysis method to obtain aspect terms in the review text. Subsequently, the method designs an aspect term embedding layer to incorporate the aforementioned external domain knowledge into the model. Finally, it utilizes an information fusion strategy based on the attention mechanism to fuse the global and local semantic information of the input text. The experimental results show that the method achieves significant improvement in both automatic evaluation metrics compared to the benchmark models.
强敏杰,王中卿,周国栋.基于预训练模型的用户评分预测.软件学报,2025,36(2):608-624
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