TY -的A2 Alonso-Betanzos帕罗AU -哈纳菲,非盟-穆罕默德Aboobaider,一向对PY - 2021 DA - 2021/12/07 TI字的顺序使用深LSTM和稀疏矩阵分解处理评级数据电子商务推荐系统SP - 8751173六世- 2021 AB -推荐系统是必不可少的引擎为电子商务企业提供产品推荐。成功采用的推荐系统可以显著影响营销的发展目标。协同过滤是一种推荐系统模型,使用客户的活动在过去,如评级。不幸的是,评级从客户收集的数量少,达不到4%。潜在因素模型是一种协同过滤涉及矩阵分解生成评级预测。然而,仅使用矩阵分解会导致不准确的建议。几个模型包括产品评审文档增加他们的评级预测的有效性。他们中的大多数使用方法如TF-IDF和LDA解释产品审查文档。然而,传统模型如LDA和TF-IDF面临一些缺点,他们表现出更少的文档的上下文理解。本研究综合矩阵分解和新颖的模型来解释和理解产品使用LSTM审查文档和词嵌入。 According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e-commerce recommender systems with sparse data issues. SN - 1687-5265 UR - https://doi.org/10.1155/2021/8751173 DO - 10.1155/2021/8751173 JF - Computational Intelligence and Neuroscience PB - Hindawi KW - ER -