A heuristic concept construction approach to collaborative recommendation

Liu, Zhonghui; Zhao, Qi; Zou, Lu; Xu, Weihua; Min, Fan

doi:10.1016/j.ijar.2022.04.004

Cited by 8 publications

(1 citation statement)

References 31 publications

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“…As a method in knowledge discovery, application of formal concept analysis and formal context covers many research domains including computer science and other domains. In computer science, some studies were successful to apply formal concept analysis for solving some problems in many sub-domains, e.g ., datamining ( Aragón, Medina & Ramírez-Poussa, 2022 ; Hao et al, 2023 ), machine learning ( Janostik, Konecny & Krajča, 2022 ), data science ( Bazin et al, 2022 ), intelligent system ( Shao et al, 2023 ), information retrieval ( Ojeda-Hernández, López-Rodríguez & Mora, 2023 ; Khattak et al, 2021 ), natural language processing ( Marín et al, 2021 ; Jain, Seeja & Jindal, 2020 ), decision support system ( Wei et al, 2020 ), recommendation system ( Liu et al, 2022 ), semantic web ( Jindal, Seeja & Jain, 2020 ), cloud computing ( Khemili, Hajlaoui & Omri, 2022 ), data structure ( Ferré & Cellier, 2020 ), mobile application ( Kwon et al, 2021 ), software engineering ( Carbonnel et al, 2020 ), and robotic ( Zhang et al, 2023 ). In addition, some successful studies to apply formal concept analysis were in other domains, e.g ., engineering ( Rocco, Hernandez-Perdomo & Mun, 2020 ), mathematics ( Jäkel & Schmidt, 2022 ; Rocco, Hernandez-Perdomo & Mun, 2020 ), biology ( Gély et al, 2022 ), psychology ( Belohlavek & Mikula, 2022 ), medicine ( Md Saleh, Ab Ghani & Jilani, 2022 ), business ( Wajnberg et al, 2018 ; Ravi, Ravi & Prasad, 2017 ; Acharjya & Das, 2017 ), and social science ( Lang & Yao, 2023 ; Hao et al, 2021 ; Gao et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Non-redundant implicational base of formal context with constraints using SAT

Hidayat,

Ahmad,

Ngo

2024

PeerJ Computer Science

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An implicational base is knowledge extracted from a formal context. The implicational base of a formal context consists of attribute implications which are sound, complete, and non-redundant regarding to the formal context. Non-redundant means that each attribute implication in the implication base cannot be inferred from the others. However, sometimes some attribute implications in the implication base can be inferred from the others together with a prior knowledge. Regarding knowledge discovery, such attribute implications should be not considered as new knowledge and ignored from the implicational base. In other words, such attribute implications are redundant based on prior knowledge. One sort of prior knowledge is a set of constraints that restricts some attributes in data. In formal context, constraints restrict some attributes of objects in the formal context. This article proposes a method to generate non-redundant implication base of a formal context with some constraints which restricting the formal context. In this case, non-redundant implicational base means that the implicational base does not contain all attribute implications which can be inferred from the others together with information of the constraints. This article also proposes a formulation to check the redundant attribute implications and encoding the problem into satisfiability (SAT) problem such that the problem can be solved by SAT Solver, a software which can solve a SAT problem. After implementation, an experiment shows that the proposed method is able to check the redundant attribute implication and generates a non-redundant implicational base of formal context with constraints.

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