有效个性化推荐的混合多准则协同过滤模型

IF 2 4区 计算机科学 Q2 Computer Science Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI:10.32604/iasc.2022.020132
Abdelrahman H. Hussein, Qasem M. Kharma, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour
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引用次数: 1

摘要

推荐系统作为决策支持系统,支持用户在过载的搜索空间中从大量选择中选择正确的项目或服务。然而,这样的系统在处理稀疏评级数据方面存在困难。处理此问题的一种方法是将附加的显式信息(也称为附带信息)合并到评级信息中。然而,这些附带信息需要用户进行一些明确的操作,而且通常并不总是可用的。因此,本研究提出了一种混合多准则协同过滤模型。该模型利用多准则评分、隐式相似度、相似传递性和全局声誉等概念来扩展潜在推荐者的空间。这种扩展将提高模型在稀疏数据情况下的预测精度和覆盖范围。为了证明所提出模型的有效性,在两个真实世界的多标准数据集Yahoo!电影和猫途鹰。实验结果表明,在各种评价指标下,与许多现有的基于协同过滤的推荐方法相比,该模型具有优越性。
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A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations
Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, implicit similarity, similarity transitivity and global reputation concepts to expand the space of potential recommenders. This expansion will enhance the prediction accuracy and coverage of the proposed model when applied to sparse data situations. To show effectiveness of the proposed model, a set of experiments are conducted on two real-world multi-criteria datasets, Yahoo! Movies and TripAdvisor. The experimental results demonstrate the superiority of the proposed model compared to a number of existing collaborative filtering-based recommendation methods under a variety of evaluation metrics.
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来源期刊
Intelligent Automation and Soft Computing
Intelligent Automation and Soft Computing 工程技术-计算机:人工智能
CiteScore
3.50
自引率
10.00%
发文量
429
审稿时长
10.8 months
期刊介绍: An International Journal seeks to provide a common forum for the dissemination of accurate results about the world of intelligent automation, artificial intelligence, computer science, control, intelligent data science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence, cyber security and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of systems engineering and soft computing. The journal will publish original and survey papers on artificial intelligence, intelligent automation and computer engineering with an emphasis on current and potential applications of soft computing. It will have a broad interest in all engineering disciplines, computer science, and related technological fields such as medicine, biology operations research, technology management, agriculture and information technology.
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