Recommendation System with Biclustering
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 exis...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020012 |
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| _version_ | 1832573634641657856 |
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| author | Jianjun Sun Yu Zhang |
| author_facet | Jianjun Sun Yu Zhang |
| author_sort | Jianjun Sun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-96cb271de3984369b8ec95f9d08914d3 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-96cb271de3984369b8ec95f9d08914d32025-02-02T03:45:08ZengTsinghua University PressBig Data Mining and Analytics2096-06542022-12-015428229310.26599/BDMA.2022.9020012Recommendation System with BiclusteringJianjun Sun0Yu Zhang1School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, ChinaNingbo Institute of Northwestern Polytechnical University, Ningbo 315103, China, and is also with School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, ChinaThe 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.https://www.sciopen.com/article/10.26599/BDMA.2022.9020012recommendation system (rs)collaborative filtering (cf)local patternbiclusteringsimilarity measure |
| spellingShingle | Jianjun Sun Yu Zhang Recommendation System with Biclustering Big Data Mining and Analytics recommendation system (rs) collaborative filtering (cf) local pattern biclustering similarity measure |
| title | Recommendation System with Biclustering |
| title_full | Recommendation System with Biclustering |
| title_fullStr | Recommendation System with Biclustering |
| title_full_unstemmed | Recommendation System with Biclustering |
| title_short | Recommendation System with Biclustering |
| title_sort | recommendation system with biclustering |
| topic | recommendation system (rs) collaborative filtering (cf) local pattern biclustering similarity measure |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020012 |
| work_keys_str_mv | AT jianjunsun recommendationsystemwithbiclustering AT yuzhang recommendationsystemwithbiclustering |