模糊概念集的启发式构造方法及其推荐应用

Heuristic construction method of fuzzy concept set and its recommended application

摘要

针对模糊形式概念分析在推荐应用中难以用于大规模数据集的问题,提出了一种基于模糊概念集启发式构造的推荐方法。根据用户之间的相似度,为每个用户构建子背景,在子背景上采用新的启发式信息,分别以用户和项目为线索生成模糊概念。利用模糊概念内部信息,设计了融入用户权重的推荐置信度,实现了对用户的个性化推荐。在6个真实数据集上进行试验,本方法的推荐效率较高,与经典的协同过滤算法相比,在稀疏的数据集上能够取得更好的推荐效果。

Abstract

Aiming at the problem that fuzzy formal concept analysis is difficult to apply to large-scale datasets in recommendation applications, a recommendation method based on a heuristic construction of fuzzy concept set is proposed. Sub-contexts are constructed for each user based on the similarity between users. Then, new heuristic information is used on the sub-contexts to generate fuzzy concepts with users and items as clues, respectively. Finally, using the internal information of fuzzy concepts, a recommendation confidence integrated with user weights is designed to achieve personalized recommendations for users. The experimental results on six real datasets show that the proposed method has higher recommendation efficiency, and can achieve better recommendation results on sparse datasets compared with classical collaborative filtering algorithms.

关键词

形式概念分析 / 模糊概念 / 概念构造 / 推荐系统 / 用户相似度

Key words

formal concept analysis / fuzzy concept / concept construction / recommender system / user similarity

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基金资助

国家自然科学基金资助项目(61976245)

中央引导地方科技发展专项资助项目(2021ZYD0003)