基于改进的密度网络模型的有效电影推荐

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS Multiagent and Grid Systems Pub Date : 2023-10-06 DOI:10.3233/mgs-230012
V. Lakshmi Chetana, Raj Kumar Batchu, Prasad Devarasetty, Srilakshmi Voddelli, Varun Prasad Dalli
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引用次数: 0

摘要

近年来,推荐系统以数据库为基础,通过歌曲、产品、电影、书籍等方式为用户提供建议。通常,电影推荐系统根据数据库中存在的属性来预测用户喜欢的电影。电影推荐系统是一个广泛的、有用的、高效的应用程序,为个人在最短的决策时间内观看电影。研究人员尝试通过开发推荐系统来解决这些问题,如购买书籍,看电影等。大多数推荐系统在处理数据稀疏性、冷启动问题和恶意攻击方面失败。为了克服上述问题,本文开发了一个新的电影推荐系统。最初,输入数据从Movielens 1M、Movielens 100K、Yahoo Y-10-10和Yahoo Y-20-20数据库获取。接下来,使用最小-最大归一化技术重新缩放数据,这有助于有效地处理离群值。最后,将去噪后的数据馈送到改进的DenseNet模型中进行相关的电影推荐,其中开发的模型包含加权因子和类平衡损失函数,以便更好地处理过拟合风险。实验结果表明,改进后的DenseNet模型在Movielens 1M、Movielens 100K、Yahoo Y-10-10和Yahoo Y-20-20数据库上,与传统模型相比,误差值几乎降低了5% ~ 10%,f-measure、精度和召回率提高了2%左右。
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Effective movie recommendation based on improved densenet model
In recent times, recommendation systems provide suggestions for users by means of songs, products, movies, books, etc. based on a database. Usually, the movie recommendation system predicts the movies liked by the user based on attributes present in the database. The movie recommendation system is one of the widespread, useful and efficient applications for individuals in watching movies with minimal decision time. Several attempts are made by the researchers in resolving these problems like purchasing books, watching movies, etc. through developing a recommendation system. The majority of recommendation systems fail in addressing data sparsity, cold start issues, and malicious attacks. To overcome the above-stated problems, a new movie recommendation system is developed in this manuscript. Initially, the input data is acquired from Movielens 1M, Movielens 100K, Yahoo Y-10-10, and Yahoo Y-20-20 databases. Next, the data are rescaled using a min-max normalization technique that helps in handling the outlier efficiently. At last, the denoised data are fed to the improved DenseNet model for a relevant movie recommendation, where the developed model includes a weighting factor and class-balanced loss function for better handling of overfitting risk. Then, the experimental result indicates that the improved DenseNet model almost reduced by 5 to 10% of error values, and improved by around 2% of f-measure, precision, and recall values related to the conventional models on the Movielens 1M, Movielens 100K, Yahoo Y-10-10, and Yahoo Y-20-20 databases.
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来源期刊
Multiagent and Grid Systems
Multiagent and Grid Systems COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
1.50
自引率
0.00%
发文量
13
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