Collaborative Filtering Recommendation Algorithm Based on Linguistic Concept Lattice with Fuzzy Object

Kuo Pang, Ning Kang, Siqi Chen, Hongliang Zheng, L. Zou
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引用次数: 1

Abstract

With the coming of the era of big data, the recommendation system has become the most important way for all industries to obtain more effective information. Aiming at the problem of recommendation fuzzy explanation in recommendation system, we propose a collaborative filtering algorithm (CF) based on linguistic concept lattice with fuzzy object. Specifically, we introduce two operators between objects and linguistic concepts, and discuss their properties. Based on that, we construct a linguistic concept lattice with fuzzy object. Furthermore, considering the similarity between objects in the linguistic formal context with fuzzy object, we also propose an aggregation operator between linguistic concepts to obtain more accurate recommendation results for users. Finally, a practical example which concerned the movie recommendation is given to intuitively illustrate the applicability and effectiveness of the algorithm.
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基于模糊对象的语言概念格协同过滤推荐算法
随着大数据时代的到来,推荐系统已经成为各行各业获取更有效信息的最重要途径。针对推荐系统中的推荐模糊解释问题,提出了一种基于模糊对象的语言概念格协同过滤算法。具体来说,我们在对象和语言概念之间引入了两个算子,并讨论了它们的性质。在此基础上,构造了一个带有模糊对象的语言概念格。此外,考虑到语言形式上下文中对象与模糊对象之间的相似性,我们还提出了语言概念之间的聚合算子,为用户获得更准确的推荐结果。最后,以电影推荐为例,直观地说明了该算法的适用性和有效性。
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