Recommender system (RS) has evolved significantly over the last few decades. This revolutionary move in RS is the adoption of machine learning algorithms from the field of artificial intelligence to produce the personalized recommendation of products or services. This literature presents an exhaustive survey on RS to emphasizes its taxonomy pertaining to diverse perspectives. This survey aims to provide a systematic review of current research in the field of a trustworthy recommendation model and identifies research opportunities to ease the problems of cold start and data sparsity. With the emergence of the internet environment, e-commerce has widely adopted this as a strategy to identify potential customers from an ever-growing volume of online information . The influence of RS has also been flourishing due to its effectiveness in information retrieval research. This article aims to expand from the exciting phase of development in the recommender systems to its utility in the current trend of pervasive online web applications.

推荐系统（RS）在过去的几十年中有了长足的发展。RS的这一革命性举措是采用人工智能领域的机器学习算法来产生产品或服务的个性化推荐。该文献对RS进行了详尽的调查，以强调其与不同观点有关的分类法。这项调查旨在对可信赖的推荐模型领域中的当前研究进行系统的综述，并确定减轻冷启动和数据稀疏性问题的研究机会。随着互联网环境的出现，电子商务已广泛采用这种策略作为从不断增长的在线信息中识别潜在客户的策略。由于RS在信息检索研究中的有效性，RS的影响也一直在蓬勃发展。本文旨在将推荐器系统的激动人心的阶段扩展到其在当今普遍使用的在线Web应用程序趋势中的效用。