Department of Computer Science and Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600 062, India.
Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 708 00 Ostrava, Czech Republic.
Sensors (Basel). 2022 Jun 29;22(13):4904. doi: 10.3390/s22134904.
Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as -means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems.
电影推荐系统旨在根据用户最喜欢的特征向他们提供建议。一个性能出色的电影推荐系统将推荐与相似度最高的电影。本研究对电影推荐系统进行了系统的文献综述。它强调了推荐系统中的过滤标准、电影推荐系统中实现的算法、性能衡量标准、实施中的挑战以及对未来研究的建议。讨论了一些最流行的机器学习算法，如均值聚类、主成分分析和自组织映射与主成分分析，并详细介绍了它们在电影推荐系统中的应用。特别强调了使用基于元启发式的推荐系统进行的研究工作。本研究旨在揭示在开发电影推荐系统方面取得的进展，以及为了减少当前实施可行解决方案的挑战需要做些什么。这篇文章将对推荐系统领域的研究人员以及参与实施此类系统的数据科学家有所帮助。
