Deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations

Online recommender systems generally suffer from severe data sparsity problems, and this are particularly prevalent in newly launched systems that do not have sufficient amounts of data. Cross-domain recommendations can provide us with some new ideas for assisting with user recommendations in sparse...

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Veröffentlicht in:Knowledge-based systems Jg. 220; S. 106948
Hauptverfasser: Li, Yakun, Ren, Jiadong, Liu, Jiaomin, Chang, Yixin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 23.05.2021
Elsevier Science Ltd
Schlagworte:
Adversarial learning
Algorithms
Data sparsity
Domains
Interlayers
Machine learning
Matrix factorization
Prediction models
Recommender systems
Regularization
Robustness
Sparse autoencoder
Sparsity
Matrix factorization
Sparse autoencoder
Data sparsity
Adversarial learning
Recommender systems
ISSN:0950-7051, 1872-7409
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Zusammenfassung:Online recommender systems generally suffer from severe data sparsity problems, and this are particularly prevalent in newly launched systems that do not have sufficient amounts of data. Cross-domain recommendations can provide us with some new ideas for assisting with user recommendations in sparse target domains by transferring knowledge from a source domain with rich data. In this paper, a deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations (DSAP-AL) is proposed to improve the accuracy of rating predictions in similar cross-domain recommender systems. Specifically, joint matrix factorization and adversarial network learning models are adopted to integrate and align user and item latent factor spaces in a unified pattern. Then, a deep sparse autoencoder is represented and modeled by transferring the latent factors and interlayer weights. Furthermore, a domain factor adaptation algorithm is proposed to capture robust user and item factors, and the learned regularization constraints are added to the objective function, thereby alleviating the data sparsity issue. Experimental results on four real-world datasets demonstrate that, even without overlapping entities (users or items) in the source and target domains, the proposed DSAP-AL method achieves competitive performance relative to other state-of-the-art individual and cross domain approaches. Moreover, the DSAP-AL model is not only effective for scenarios with sparse data but also robust for noise-containing recommendations.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.106948