In recent years, several researchers have developed web-based product recommendation systems to assist customers in product search and selection during online shopping. In addition, the product recommendation systems deliver true personalization by recommending the products based on the other customer’s preferences. This study has investigated how the product recommendation system influences the customer’s decision effort and quality. In this study, the proposed system comprises of five major phases: data collection, pre-processing, key word extraction, keyword optimization and similar data clustering. The input data were collected from amazon customer review dataset. After the data collection, pre-processing was carried-out to enhance the quality of collected amazon data. The pre-processing phase comprises of two systems lemmatization and removal of stop-words & uniform resource locators (URLs). Then, a superior topic modelling method Latent Dirichlet allocation (LDA) along with modified grey wolf optimizer (MGWO) was applied in order to identify the optimal keywords. The extracted key-words were clustered into two forms (positive and negative) by applying a clustering algorithm named as quantum inspired possibilistic fuzzy C-means (QIPFCM). Experimental results showed that the proposed system achieved better performance in the product recommendation system compared to the existing systems in terms of accuracy, precision, recall and f-measure.

近年来，一些研究人员开发了基于Web的产品推荐系统，以帮助客户在在线购物期间进行产品搜索和选择。此外，产品推荐系统通过根据其他客户的偏好来推荐产品，从而实现真正的个性化。这项研究调查了产品推荐系统如何影响客户的决策工作和质量。在这项研究中，提出的系统包括五个主要阶段：数据收集，预处理，关键词提取，关键词优化和类似数据聚类。输入数据是从亚马逊客户评论数据集中收集的。数据收集之后，进行了预处理以提高所收集的亚马逊数据的质量。预处理阶段包括两个系统的词素化和停用词和统一资源定位符（URL）的删除。然后，采用了高级主题建模方法Latent Dirichlet分配（LDA）以及改进的灰狼优化器（MGWO）来识别最佳关键字。通过应用称为量子启发可能模糊C均值（QIPFCM）的聚类算法，将提取出的关键词聚类为两种形式（正负）。实验结果表明，与现有系统相比，该系统在产品推荐系统中的准确性，精确度，召回率和f量度均有所提高。为了识别最佳关键字，应用了高级主题建模方法Latent Dirichlet分配（LDA）和改进的灰太狼优化器（MGWO）。通过应用称为量子启发可能模糊C均值（QIPFCM）的聚类算法，将提取出的关键词聚类为两种形式（正负）。实验结果表明，与现有系统相比，所提出的系统在产品推荐系统中的准确性，准确性，召回率和f-measure都有较好的表现。为了识别最佳关键字，应用了高级主题建模方法Latent Dirichlet分配（LDA）和改进的灰太狼优化器（MGWO）。通过应用称为量子启发可能模糊C均值（QIPFCM）的聚类算法，将提取的关键字聚类为两种形式（正负）。实验结果表明，与现有系统相比，所提出的系统在产品推荐系统中的准确性，准确性，召回率和f-measure都有较好的表现。