The trend of data mining using deep learning models on graph neural networks has proven effective in identifying object features through signal encoders and decoders, particularly in recommendation systems utilizing collaborative filtering methods. Collaborative filtering exploits similarities between users and items from historical data. However, it overlooks distinctive information, such as item names and descriptions. The semantic data of items should be further mined using models in the natural language processing field. Thus, items can be compared using text classification, similarity assessments, or identifying analogous sentence pairs. This research proposes combining two sources of item similarity signals: one from collaborative filtering and one from the semantic similarity measure between item names and descriptions. These signals are integrated into a graph convolutional neural network to optimize model weights, thereby providing accurate recommendations. Experiments are also designed to evaluate the contribution of each signal group to the recommendation results.

翻译：基于图神经网络的深度学习模型进行数据挖掘的趋势，在通过信号编码器和解码器识别对象特征方面已被证明是有效的，尤其是在利用协同过滤方法的推荐系统中。协同过滤利用从历史数据中提取的用户与物品之间的相似性。然而，它忽略了诸如物品名称和描述等独特信息。物品的语义数据应使用自然语言处理领域的模型进行进一步挖掘。因此，可以通过文本分类、相似性评估或识别相似句子对来比较物品。本研究提出结合两种物品相似性信号源：一种来自协同过滤，另一种来自物品名称和描述之间的语义相似性度量。这些信号被整合到一个图卷积神经网络中，以优化模型权重，从而提供准确的推荐。同时设计了实验来评估每组信号对推荐结果的贡献。