基于有序加权聚合的以人为中心的排序推荐

IF 3.8 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
S. S. Sohail, Asfia Aziz, R. Ali, S. H. Hasan, D. Madsen, M. Alam
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引用次数: 0

摘要

在本文中,我们提出了一种推荐系统的方法,该方法通过有序加权聚合(OWA)结合以人为中心的聚合,将专家排名者的建议优先于通常的推荐。我们提倡排名推荐,其中排名者根据其排名位置分配权重。我们的方法是使用语言数据摘要和OWA技术向大学生推荐书籍。我们为排名最高的大学分配更高的权重,以提高推荐质量。我们的方法在八个参数上进行了评估,并且优于传统的推荐系统。我们声称,我们的方法节省了存储空间,解决了冷启动问题,不需要事先的用户偏好。该方法可以应用于决策问题,特别是推荐系统的决策问题,为推荐研究中针对人类的任务聚合提供了新的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human-Centric Aggregation via Ordered Weighted Aggregation for Ranked Recommendation in Recommender Systems
In this paper, we propose an approach to recommender systems that incorporates human-centric aggregation via Ordered Weighted Aggregation (OWA) to prioritize the suggestions of expert rankers over the usual recommendations. We advocate for ranked recommendations where rankers are assigned weights based on their ranking position. Our approach recommends books to university students using linguistic data summaries and the OWA technique. We assign higher weights to the highest-ranked university to improve recommendation quality. Our approach is evaluated on eight parameters and outperforms traditional recommender systems. We claim that our approach saves storage space and solves the cold start problem by not requiring prior user preferences. Our proposed scheme can be applied to decision-making problems, especially in the context of recommender systems, and offers a new direction for human-specific task aggregation in recommendation research.
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来源期刊
Applied System Innovation
Applied System Innovation Mathematics-Applied Mathematics
CiteScore
7.90
自引率
5.30%
发文量
102
审稿时长
11 weeks
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