Online reviews play a crucial role in shaping consumer decisions, especially in the context of e-commerce. However, the quality and reliability of these reviews can vary significantly. Some reviews contain misleading or unhelpful information, such as advertisements, fake content, or irrelevant details. These issues pose significant challenges for recommendation systems, which rely on user-generated reviews to provide personalized suggestions. This article introduces a recommendation system based on Passer Learning Optimization-enhanced Bi-LSTM classifier applicable to e-commerce recommendation systems with improved accuracy and efficiency compared to state-of-the-art models. It achieves as low as 1.24% MSE on the baby dataset. This lifts it as high as 88.58%. Besides, there is also robust performance of the system on digital music and patio lawn garden datasets at F1 of 88.46% and 92.51%, correspondingly. These results, made possible by advanced graph embedding for effective knowledge extraction and fine-tuning of classifier parameters, establish the suitability of the proposed model in various e-commerce environments.

翻译：在线评论在影响消费者决策方面发挥着至关重要的作用，尤其在电子商务环境中。然而，这些评论的质量和可靠性存在显著差异。部分评论包含误导性或无效信息，例如广告、虚假内容或不相关细节。这些问题对依赖用户生成评论以提供个性化建议的推荐系统构成了重大挑战。本文提出了一种基于Passer学习优化增强的Bi-LSTM分类器的推荐系统，适用于电子商务推荐场景，相较于现有先进模型，在准确性和效率上均有提升。该系统在婴儿产品数据集上实现了低至1.24%的均方误差，并将性能提升至高达88.58%。此外，该系统在数字音乐及庭院草坪花园数据集上也表现出稳健性能，F1分数分别达到88.46%和92.51%。这些成果得益于先进的图嵌入技术以实现有效的知识提取，以及对分类器参数的精细调优，从而证明了所提模型在多种电子商务环境中的适用性。