Performance analysis of neural networks-based multi-criteria recommender systems

Mohammed Hassan, Mohamed Hamada
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引用次数: 5

Abstract

Frequent use of Internet applications and rapid growth of volumes of online resources have made it difficult for users to effectively make decisions on the kinds of information or items to select. Recommender systems (RSs) are intelligent decision-support tools that exploit users' preferences and suggest items that might be interesting to them. They are one of the various solutions used by online users to overcome the problem of information overload. Traditionally, RSs use single ratings to predict and represent preferences of users for items that are not yet seen. Multi-criteria RSs use multiple ratings to various items' attributes for improving prediction and recommendation accuracy of the systems. However, one major challenge of multi-criteria RSs is the choice of an efficient approach for modelling the criteria ratings. Therefore, this paper aimed at employing artificial neural networks to model the criteria ratings and determine the predictive performance of the systems based on aggregation function approach. Seven evaluation metrics have been used to evaluate and the accuracy of the systems. The empirical results of the study have shown that the proposed technique has the highest prediction and recommendation than the corresponding traditional technique.
基于神经网络的多准则推荐系统性能分析
互联网应用程序的频繁使用和在线资源数量的快速增长使得用户很难有效地决定要选择的信息或项目的种类。推荐系统(RSs)是一种智能决策支持工具,它利用用户的偏好并建议他们可能感兴趣的项目。它们是在线用户用来克服信息过载问题的各种解决方案之一。传统上,RSs使用单个评级来预测和表示用户对尚未看到的项目的偏好。多标准RSs对不同项目的属性使用多个评级,以提高系统的预测和推荐精度。然而,多标准RSs的一个主要挑战是选择一种有效的方法来对标准评级进行建模。因此,本文旨在利用人工神经网络对标准评级进行建模,并基于聚合函数方法确定系统的预测性能。七个评价指标已被用来评估和系统的准确性。实证研究结果表明,本文提出的技术比相应的传统技术具有最高的预测和推荐率。
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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