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Open Access

Recommendation System with Biclustering

School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Ningbo Institute of Northwestern Polytechnical University, Ningbo 315103, China, and is also with School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
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Abstract

The massive growth of online commercial data has raised the request for an automatic recommender system to benefit both users and merchants. One of the most frequently used recommendation methods is collaborative filtering, but its accuracy is limited by the sparsity of the rating dataset. Most existing collaborative filtering methods consider all features when calculating user/item similarity and ignore much local information. In collaborative filtering, selecting neighbors and determining users’ similarities are the most important parts. For the selection of better neighbors, this study proposes a novel biclustering method based on modified fuzzy adaptive resonance theory. To reflect the similarity between users, a new measure that considers the effect of the number of users’ common items is proposed. Specifically, the proposed novel biclustering method is first adopted to obtain local similarity and local prediction. Second, item-based collaborative filtering is used to generate global predictions. Finally, the two resultant predictions are fused to obtain a final one. Experiment results demonstrate that the proposed method outperforms state-of-the-art models in terms of several aspects on three benchmark datasets.

Keywords

Collaborative Filtering (CF)Recommendation System (RS)local patternbiclusteringsimilarity measure

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Big Data Mining and Analytics
Volume 5 Issue 4,
December 2022
Pages 282-293
DOI: 10.26599/BDMA.2022.9020012
Cite this article:
Sun J, Zhang Y. Recommendation System with Biclustering. Big Data Mining and Analytics, 2022, 5(4): 282-293. https://doi.org/10.26599/BDMA.2022.9020012

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Received: 06 September 2021
Revised: 02 March 2022
Accepted: 05 May 2022
Published: 18 July 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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