Implementation of a Collaborative Recommendation System Based on Multi-Clustering

Lili Wang, Sunit Mistry (), Abdulkadir Abdulahi Hasan, Abdiaziz Omar Hassan, Yousuf Islam and Frimpong Atta Junior Osei
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Lili Wang: School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232000, China
Sunit Mistry: School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232000, China
Abdulkadir Abdulahi Hasan: School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232000, China
Abdiaziz Omar Hassan: International School of Design, Zhejiang University, Ningbo 315100, China
Yousuf Islam: School of Physics and Electronics, Central South University, Changsha 410083, China
Frimpong Atta Junior Osei: Department of Computer Science, University of Oregon, Eugene, OR 97403, USA

Mathematics, 2023, vol. 11, issue 6, 1-21

Abstract: The study aims to present an architecture for a recommendation system based on user items that are transformed into narrow categories. In particular, to identify the movies a user will likely watch based on their favorite items. The recommendation system focuses on the shortest connections between item correlations. The degree of attention paid to user-group relationships provides another valuable piece of information obtained by joining the sub-groups. Various relationships have been used to reduce the data sparsity problem. We reformulate the existing data into several groups of items and users. As part of the calculations and containment of activities, we consider Pearson similarity, cosine similarity, Euclidean distance, the Gaussian distribution rule, matrix factorization, EM algorithm, and k-nearest neighbors (KNN). It is also demonstrated that the proposed methods could moderate possible recommendations from diverse perspectives.

Keywords: recommendation system; collaborative filtering; movie; multi-clustering; k-nearest neighbors (KNN) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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