The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix Factorization
Abstract
:1. Introduction
- (1)
- Proposing a recommendation algorithm based on an improved CVAE and CPMF, which effectively addresses the issues of poor recommendation performance in traditional user-based collaborative filtering recommendation algorithms due to data rating sparsity and suboptimal feature extraction.
- (2)
- Enhancements are made to the CVAE. In addition to the existing structure, latent layers are incorporated, and random noise is introduced into these layers. Processing input data through these latent layers yields more precise implicit features, thereby enhancing the model’s resilience to interference and its generative capabilities. Furthermore, the CVAE incorporates item categories as auxiliary information for supervising the encoding and decoding of item data. By learning the distribution characteristics of data, it effectively reconstructs missing values in rating data, reducing data sparsity. The reconstructed data is subsequently subjected to CPMF to extract implicit rating features of users and items, optimizing the feature extraction process.
- (3)
- MovieLens-100K and MovieLens-1M datasets are selected for experimental evaluation. Four comparative algorithms are chosen, and the proposed algorithm is compared to them using the same dataset. The comparison results reveal that the proposed algorithm reduces the Root Mean Square Error (RMSE) by 2.26%, 3.30%, 2.57%, and 5.12%, and the Mean Absolute Error (MAE) by 2.63%, 3.66%, 2.53%, and 5.33% respectively. In the MovieLens-1M dataset, RMSE is reduced by 4.39%, 5.49%, 4.22%, and 5.99%, and MAE is reduced by 3.28%, 4.17%, 3.08%, and 5.97%, respectively.
2. Correlation Algorithm
2.1. User-Based Collaborative Filtering Recommendation
- (1)
- Construction of User-Item Matrix
- (2)
- Calculation of User Similarity
- (3)
- Identifying Nearest Neighbors
- (4)
- Predict Ratings and Generate Recommendations
2.2. Autoencoder
2.3. Matrix Factorization
3. Methodology
3.1. Improved Data Reconstruction for CVAE
3.2. CPMF
3.3. Algorithm Flow
4. Experiment and Analysis
4.1. Experimental Data
4.2. Evaluation Index
4.3. Experimental Results and Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Dataset Information | MovieLens-100K | MovieLens-1M |
---|---|---|
Number of users | 943 | 6040 |
Number of movies | 1682 | 3544 |
Movie Categories | unknown|Action|Adventure|Animation|Children’s|Comedy|Crime|Documentary… | |
Range of ratings | 1~5 | |
Rating items | 100,000 | 993,482 |
Comparison Method | RMSE | MAE |
---|---|---|
CDL | 0.9234 | 0.7485 |
CTR | 0.9338 | 0.7588 |
PHD | 0.9265 | 0.7475 |
PMF | 0.9520 | 0.7755 |
This Paper | 0.9008 | 0.7222 |
Comparison Method | RMSE | MAE |
---|---|---|
CDL | 0.8922 | 0.7183 |
CTR | 0.9032 | 0.7272 |
PHD | 0.8905 | 0.7163 |
PMF | 0.9082 | 0.7452 |
This Paper | 0.8483 | 0.6855 |
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Zhang, Y.; Xu, H.; Yu, X. The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix Factorization. Appl. Sci. 2023, 13, 12027. https://doi.org/10.3390/app132112027
Zhang Y, Xu H, Yu X. The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix Factorization. Applied Sciences. 2023; 13(21):12027. https://doi.org/10.3390/app132112027
Chicago/Turabian StyleZhang, Yunfei, Hongzhen Xu, and Xiaojun Yu. 2023. "The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix Factorization" Applied Sciences 13, no. 21: 12027. https://doi.org/10.3390/app132112027
APA StyleZhang, Y., Xu, H., & Yu, X. (2023). The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix Factorization. Applied Sciences, 13(21), 12027. https://doi.org/10.3390/app132112027