Abstract With the new era of the Internet, we have a large amount of data available in the form of ratings, reviews, graphs, images, etc. However, still, people face difficulty in finding useful information or knowledge from those data. To address these challenges, recommendation systems come into the picture by providing useful content to the user based on users’ history and similarity among users. Content-based and collaborative filtering are two major building blocks of recommendation systems. Recommendation systems have been applied into numbers of a domain such as recommending movies, music, course, literature, items, people, links, location, healthcare, agriculture. In the agriculture domain, appropriate crops to cultivate and selecting applicable pesticides based on land quality and types of crops are interesting factors to consider for a country like India. Initially, we review different types of recommendation systems along with its application area. Subsequently, we explore various parameters to evaluate recommendation systems followed by open issues and research challenges. We further study the work carried out by existing researchers in the said domain. As part of our contribution through this research, we have selected the Agriculture domain and proposed our algorithm for recommending crops based on various parameters. As an outcome of our contribution, a crop is recommended to farmers based on his land. Also, the system recommends a list of lands for a given crop. Using statistical analysis, we achieve accuracy from 93% to 97%.

中文翻译：

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关于推荐系统和预期扩展的最新调查

摘要 随着互联网的新时代，我们拥有大量的数据，如评分、评论、图表、图像等。然而，人们仍然难以从这些数据中找到有用的信息或知识。为了应对这些挑战，推荐系统通过根据用户的历史记录和用户之间的相似性向用户提供有用的内容来发挥作用。基于内容和协同过滤是推荐系统的两个主要构建块。推荐系统已被应用于多个领域，例如推荐电影、音乐、课程、文学、物品、人物、链接、位置、医疗保健、农业。在农业领域，根据土地质量和作物类型选择合适的作物来种植和选择适用的杀虫剂是像印度这样的国家需要考虑的有趣因素。最初，我们回顾了不同类型的推荐系统及其应用领域。随后，我们探索了各种参数来评估推荐系统，然后是开放问题和研究挑战。我们进一步研究了现有研究人员在该领域开展的工作。作为我们通过这项研究所做贡献的一部分，我们选择了农业领域，并提出了我们的算法，用于根据各种参数推荐作物。作为我们贡献的结果，根据他的土地向农民推荐一种作物。此外，系统会为给定作物推荐土地清单。使用统计分析，我们实现了 93% 到 97% 的准确率。