FCNHSMRA_HRS：使用资源分配方法提高电影混合推荐系统的性能
FCNHSMRA_HRS: Improve the performance of the movie hybrid recommender system using resource allocation approach
摘要：
推荐系统是能够向用户提供最合适的服务和产品的系统。通过特定的方法和技术，推荐系统试图识别最合适的项目，如信息和商品的类型，并提出最接近用户的口味。协同过滤是推荐系统中最简单、最容易理解和最成功的发现具有相同品味的人的模型之一，它根据一组用户的评分提供主动的用户建议。在这种模式下，随着用户和电影数量的增加，系统的可扩展性受到限制。另一方面，重要的是要提高系统的性能时，有很少的信息可用的评级。本文提出了一个基于FNHSM_HRS结构的电影混合推荐系统FCNHSMRA_HRS，该系统采用资源分配方法。FNHSM_HRS结构基于启发式相似性度量（NHSM）和模糊聚类。在建议的系统中使用模糊聚类方法改善了可扩展性问题，并增加了系统建议的准确性。提出的系统是基于协同过滤，并通过使用启发式相似性度量和应用资源分配方法，提高了系统的性能，准确性和精度。基于MovieLens数据集的MAE、Accuracy、Precision和Recall指标的实验结果表明，与FNHSM_HRS和使用其他相似性度量的协同过滤方法相比，系统的性能得到了改善，推荐的准确率也有所提高
英文摘要：
Recommender systems are systems that are capable of offering the most suitable services and products to users. Through specific methods and techniques, the recommender systems try to identify the most appropriate items, such as types of information and goods and propose the closest to the user's tastes. Collaborative filtering offering active user suggestions based on the rating of a set of users is one of the simplest and most comprehensible and successful models for finding people in the same tastes in the recommender systems. In this model, with increasing number of users and movie, the system is subject to scalability. On the other hand, it is important to improve the performance of the system when there is little information available on the ratings. In this paper, a movie hybrid recommender system based on FNHSM_HRS structure using resource allocation approach called FCNHSMRA_HRS is presented. The FNHSM_HRS structure was based on the heuristic similarity measure (NHSM), along with fuzzy clustering. Using the fuzzy clustering method in the proposed system improves the scalability problem and increases the accuracy of system suggestions. The proposed systems is based on collaborative filtering and, by using the heuristic similarity measure and applying the resource allocation approach, improves the performance, accuracy and precision of the system. The experimental results using MAE, Accuracy, Precision and Recall metrics based on MovieLens dataset show that the performance of the system is improved and the accuracy of recommendations in comparison of FNHSM_HRS and collaborative filtering methods that use other similarity measures for finding similarity, is increased
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
