Optimisation of UCB algorithm based on cultural content orientation of film and television in the digital era

Q4 Decision Sciences
Bin Li
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引用次数: 0

Abstract

To improve the effect of the upper confidence bound (UCB) algorithm in the recommendation of online courses of film and television culture, the paper proposes the recommendation method with time-varying Linucb. Firstly, the time-varying Linucb is introduced, and the UCB is optimised by using the attention mechanism and the short-term and short-term memory network. The results show that the recommendation accuracy of the improved model reaches up to 93%, and the novelty is basically stable at 70%. Compared with UCB, the average course viewing time of users has been extended by two hours, and the average course registration rate has remained stable at over 84%. This indicates that the improved recommendation model has excavated the diverse learning needs of users and can provide accurate course recommendation services for users, which is conducive to optimising the effectiveness of film and television cultural education.
数字时代基于影视文化内容导向的UCB算法优化
为了提高上置信度界(UCB)算法在影视文化在线课程推荐中的效果,本文提出了时变Linucb推荐方法。首先,引入时变Linucb,利用注意机制和短时记忆网络对ucucb进行优化。结果表明,改进模型的推荐准确率可达93%,新颖性基本稳定在70%。与UCB相比,用户的平均课程观看时间延长了2小时,平均课程注册率稳定在84%以上。这说明改进后的推荐模型挖掘了用户多样化的学习需求,能够为用户提供精准的课程推荐服务,有利于优化影视文化教育的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Networking and Virtual Organisations
International Journal of Networking and Virtual Organisations Decision Sciences-Information Systems and Management
CiteScore
1.40
自引率
0.00%
发文量
25
期刊介绍: IJNVO is a forum aimed at providing an authoritative refereed source of information in the field of Networking and Virtual Organisations.
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