基于标准共振关系和Choquet操作的推荐系统

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING International Journal of Data Warehousing and Mining Pub Date : 2020-10-01 DOI:10.4018/ijdwm.2020100103
H. Huynh, Le Hoang Son, Cu Nguyen Giap, T. Huynh, H. H. Luong
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引用次数: 1

摘要

由于用户的多样化需求,推荐系统在生活的各个方面变得越来越重要。推荐系统的主要目标之一是根据标准做出决定。因此,重要的是要有一个合理的解决方案,符合用户的需求和存储数据的特征。本文提出了一种基于用户准则与Choquet操作的共振关系的推荐方法,用于构建决策模型。并在基于R语言的multirecsys工具上进行了评估。通过实验证明,该模型的输出是有效可靠的。它可以在适当的环境中应用,以提高效率并最大限度地减少当前推荐系统的局限性。
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Recommender Systems Based on Resonance Relationship of Criteria With Choquet Operation
Recommender systems are becoming increasingly important in every aspect of life for the diverse needs of users. One of the main goals of the recommender system is to make decisions based on criteria. It is thus important to have a reasonable solution that is consistent with user requirements and characteristics of the stored data. This paper proposes a novel recommendation method based on the resonance relationship of user criteria with Choquet Operation for building a decision-making model. It has been evaluated on the multirecsys tool based on R language. Outputs from the proposed model are effective and reliable through the experiments. It can be applied in appropriate contexts to improve efficiency and minimize the limitations of the current recommender systems.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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