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An effective content boosted collaborative filtering for movie recommendation systems using density based clustering with artificial flora optimization algorithm
International Journal of System Assurance Engineering and Management Pub Date : 2021-06-03 , DOI: 10.1007/s13198-021-01101-2
Jayaraman Parthasarathy , Ramesh Babu Kalivaradhan

Recommender systems (RS) are information filtering approaches that intend to foresee the rating for customers and products, mainly from big data for recommending their likes. Movie RS offers a process of assisting customers in the classification of customers with identical preferences. It makes the RS an essential part of websites and e-commerce applications. Many of the classical RS lack accuracy in the case where data utilized in the recommendation task is sparse. This paper aims to develop a movie RS by the use of density based clustering (DBC) with artificial flora (AF) called DBC-AF technique. Besides, to get rid of the sparsity problem, the content-boosted collaborative filtering technique is employed to the proposed DBC-AF based movie RS. The presented model considers the content information of the movies while computing the item similarities. The proposed DBC-AF technique improved the movie prediction accuracy and developed an integrated model combining the customer rating from Movie Lens dataset for the prediction process. The effective performance of the DBC-AF model has been evaluated utilizing the Movie lens dataset. The obtained simulation outcome indicated the effective performance of the presented DBC-AF model.



中文翻译:

使用基于密度的聚类和人工植物群优化算法的电影推荐系统的有效内容增强协同过滤

推荐系统(RS)是一种信息过滤方法,旨在预测客户和产品的评级,主要来自大数据以推荐他们的喜好。Movie RS 提供了帮助客户对具有相同偏好的客户进行分类的过程。它使 RS 成为网站和电子商务应用程序的重要组成部分。在推荐任务中使用的数据稀疏的情况下,许多经典 RS 缺乏准确性。本文旨在通过使用基于密度的聚类 (DBC) 和人工植物群 (AF) 来开发电影 RS,称为 DBC-AF 技术。此外,为了摆脱稀疏性问题,内容增强的协同过滤技术被用于提出的基于 DBC-AF 的电影 RS。所提出的模型在计算项目相似度的同时考虑了电影的内容信息。提出的 DBC-AF 技术提高了电影预测的准确性,并开发了一个结合来自 Movie Lens 数据集的客户评分的集成模型,用于预测过程。DBC-AF 模型的有效性能已经利用电影镜头数据集进行了评估。获得的模拟结果表明了所提出的 DBC-AF 模型的有效性能。

更新日期:2021-06-04
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