원문정보
초록
영어
The metadata information of users and items for enhancing the recommendation system robustness has important valuable. Following this design philosophy, this paper first presents the user suspects assessment strategy based on Probabilistic Latent Semantic Analysis, the user suspected sexual and generic items such as meta-information to model parameters and Logistic Regression way into Bayesian probabilistic matrix factorization (BPMF) model, and then proposes Metadata-enhanced Variational Bayesian Matrix Factorization (MVBMF), designed a model of incremental learning strategy based on robust linear regression, in order to reduce the demand for model rebuilding. Experimental results show that MVBMF can effectively defend against shilling attacks and also has a high level of performance for strong and weak generalization.
목차
1. Introduction
2. Metadata-enhanced Variational Bayesian Matrix Factorization model (MVBMF)
2.1. Users Suspicion Assessment
2.2. The Formal Description of MVBMF
2.3. The Sampling Generated Semantic of MVBMF
2.4. Robust Security Mechanism of MVBMF
2.5. The Incremental Learning of MVBMF
3. Experimental Analysis and Results
3.1. Data Sets and Experimental Setup
3.2. Dimension Selection
3.3. Weak Generalization Situation
3.4. Strong Generalization Situation
5. Conclusion
References