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.
The PMF model is effective for addressing high-dimensional, large-scale, sparse, and imbalanced rating data, yet it may suffer from limitations in generalization and prediction accuracy in certain scenarios. To address these limitations, we propose a hybrid AdaBoost ensemble method within the PMF model. In this paper, we use two-stage algorithms in the model. Our approach uses a two-stage algorithm, whereby the first stage involves fuzzy clustering to calculate the scoring matrix of user-items, followed by neural network training to further enhance scoring prediction accuracy. The second stage involves using the rating matrix as the basis learner for training by different neural networks, and the final score prediction result is obtained through ensemble learning. Our proposed model was evaluated on the MovieLens and FilmTrust datasets, and its effectiveness was demonstrated. Due to its well-crafted architecture and robust representation learning capability, our model can be readily applied to various PMF model settings, such as PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models. The experiments show that the mean absolute error(MAE) of the proposed method increases by 1.24% and 0.79% compared with the Bagging-BP-PMF model on two different datasets, and the root mean square error(RMSE) increases by 2.55% and 1.87%, respectively. Finally, our experiments show that our proposed approach performs well in various settings. By utilizing ensemble learning to train the weight of the base learner from different neural networks, our method improves the stability of score prediction. Additionally, our results verify the universality of our approach.
With the improvement of System-on-Chip integration, the chip requires an increasingly large amount of test data. To solve the contradiction between the storage capacity and bandwidth of automatic test equipment (ATE), a new method of test data compression/decompression is proposed based on an annular scan chain. Corresponding fault bits of different test patterns are incompatible, moving test patterns in an annular scan chain, makes all of the new corresponding bits of different test patterns be compatible or backward-compatible, so different adjacent test patterns form a new relation that are indirectly compatible or indirectly backward-compatible, achieves the purpose of test data compression by encoding these indirectly compatible test patterns or indirectly backward-compatible test patterns. According to experimental results, the average compression ratio increases by %6.94 to % 15.1 compared with the other schemes, relative decompression architecture is simple. In the annular scan chain, the test pattern moves clockwise with the minimal bits, generating subsequent test patterns quickly, it is advantageous to reduce the test application time of a single IP core.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.