Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration
In recent years, recommendation systems have already played a significant role in major streaming video platforms.The probabilistic matrix factorization (PMF) model has advantages in addressing high-dimension problems and rating data sparsity in the recommendation system. However, in practical appli...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-04-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1338.pdf |
| _version_ | 1797840734246666240 |
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| author | Zhengjin Zhang Qilin Wu Yong Zhang Li Liu |
| author_facet | Zhengjin Zhang Qilin Wu Yong Zhang Li Liu |
| author_sort | Zhengjin Zhang |
| collection | DOAJ |
| description | In recent years, recommendation systems have already played a significant role in major streaming video platforms.The probabilistic matrix factorization (PMF) model has advantages in addressing high-dimension problems and rating data sparsity in the recommendation system. However, in practical application, PMF has poor generalization ability and low prediction accuracy. For this reason, this article proposes the Hybrid AdaBoost Ensemble Method. Firstly, we use the membership function and the cluster center selection in fuzzy clustering to calculate the scoring matrix of the user-items. Secondly, the clustering user items’ scoring matrix is trained by the neural network to improve the scoring prediction accuracy further. Finally, with the stability of the model, the AdaBoost integration method is introduced, and the score matrix is used as the base learner; then, the base learner is trained by different neural networks, and finally, the score prediction is obtained by voting results. In this article, we compare and analyze the performance of the proposed model on the MovieLens and FilmTrust datasets. In comparison with the PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models, several experiments show that the mean absolute error of the proposed model increases by 1.24% and 0.79% compared with Bagging-BP-PMF model on two different datasets, and the root-mean-square error increases by 2.55% and 1.87% respectively. Finally, we introduce the weights of different neural network training based learners to improve the stability of the model’s score prediction, which also proves the method’s universality. |
| first_indexed | 2024-04-09T16:19:31Z |
| format | Article |
| id | doaj.art-991729e1fd4b496a926047a77dd5e146 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-09T16:19:31Z |
| publishDate | 2023-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-991729e1fd4b496a926047a77dd5e1462023-04-23T15:05:21ZengPeerJ Inc.PeerJ Computer Science2376-59922023-04-019e133810.7717/peerj-cs.1338Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integrationZhengjin Zhang0Qilin Wu1Yong Zhang2Li Liu3Chaohu University, Hefei, ChinaChaohu University, Hefei, ChinaChaohu University, Hefei, ChinaMacau University of Science and Technology, Macau, ChinaIn recent years, recommendation systems have already played a significant role in major streaming video platforms.The probabilistic matrix factorization (PMF) model has advantages in addressing high-dimension problems and rating data sparsity in the recommendation system. However, in practical application, PMF has poor generalization ability and low prediction accuracy. For this reason, this article proposes the Hybrid AdaBoost Ensemble Method. Firstly, we use the membership function and the cluster center selection in fuzzy clustering to calculate the scoring matrix of the user-items. Secondly, the clustering user items’ scoring matrix is trained by the neural network to improve the scoring prediction accuracy further. Finally, with the stability of the model, the AdaBoost integration method is introduced, and the score matrix is used as the base learner; then, the base learner is trained by different neural networks, and finally, the score prediction is obtained by voting results. In this article, we compare and analyze the performance of the proposed model on the MovieLens and FilmTrust datasets. In comparison with the PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models, several experiments show that the mean absolute error of the proposed model increases by 1.24% and 0.79% compared with Bagging-BP-PMF model on two different datasets, and the root-mean-square error increases by 2.55% and 1.87% respectively. Finally, we introduce the weights of different neural network training based learners to improve the stability of the model’s score prediction, which also proves the method’s universality.https://peerj.com/articles/cs-1338.pdfNeural NetworkPMFAdaBoostEnsemble Learning |
| spellingShingle | Zhengjin Zhang Qilin Wu Yong Zhang Li Liu Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration PeerJ Computer Science Neural Network PMF AdaBoost Ensemble Learning |
| title | Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration |
| title_full | Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration |
| title_fullStr | Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration |
| title_full_unstemmed | Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration |
| title_short | Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration |
| title_sort | movie recommendation model based on probabilistic matrix decomposition using hybrid adaboost integration |
| topic | Neural Network PMF AdaBoost Ensemble Learning |
| url | https://peerj.com/articles/cs-1338.pdf |
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