关键词: 形式概念分析, 约简形式背景, 属性兴趣度, 概念构造, 推荐系统

Abstract: In the field of Formal Concept Analysis (FCA), the proposal of concept sets satisfies the recommendation needs of real environments. At present, concept sets generation methods lack effective means to avoid the inclusion of redundant attributes, which to some extent affects the quality and efficiency of concept generation, and ultimately the effectiveness of recommendations. To solve the above problem, a Formal Context Attribute Reduction Algorithm (FCAR), a Concept Set Construction Algorithm (CSCA), and a Recommendation Algorithm Based on Concept Set (RACS) were proposed. Firstly, the attribute interest degree was designed based on the formal context and rating matrix, and the formal context reduction was achieved according to the threshold of attribute interest degree. Secondly, by combining extent similarity and intent interest degree, the concept criticality was designed as heuristic information to generate the concept set. Finally, the recommendation matrix of the concept set was obtained using the recommendation confidence and recommendation threshold, enabling personalized recommendations for the target user. The RACS algorithm is compared with kNN, IBCF, and formal concept recommendation algorithms GRHC, CSPR, and GreConD-kNN on 11 datasets. In experiments based on six standard datasets, the RACS algorithm achieves the highest accuracy and the second highest recall on three different datasets, and it achieves the best F1 score on four datasets. Especially on three larger-scale datasets, compared to formal concept recommendation algorithms, its average recommendation time efficiency is improved by nearly 59 times. The experimental results validate the significant advantages of the RACS algorithm in recommendation effectiveness and efficiency.

Key words: Formal Concept Analysis (FCA), reduced formal context, attribute interest degree, concept construction, recommendation system