Gaussian process latent variable model factorization for context-aware recommender systems

Abstract

• We propose a novel algorithm named GPLVMF to achieve a Bayesian factorization for Context-aware recommendation. • We model the non-zero mean in Gaussian processes to capture the bias and provide a generalized variational inference. • Our method can flexibly deal with both real-valued and categorical contexts. • Both scaled conjugate gradient and stochastic gradient descent optimization methods are applied to the model. • Experiment results show that GPLVMF not only improves accuracy but can automatically infer the influence of context. Context-aware recommender systems (CARS) have gained increasing attention due to their ability to utilize contextual information. Compared to traditional recommender systems, CARS are, in general, able to generate more accurate recommendations. The latent factors approach accounts for a large proportion of CARS. Recently, a non-linear Gaussian Process (GP) based factorization method was proven to outperform the state-of-the-art methods in CARS. Despite its effectiveness, standard GP model-based methods can suffer from over-fitting and may not be able to determine the impact of each context automatically. In order to address such shortcomings, we propose a Gaussian Process Latent Variable Model Factorization (GPLVMF) method, where we apply an appropriate prior to the original GP model. Our work is primarily inspired by the Gaussian Process Latent Variable Model (GPLVM), which is a non-linear dimensionality reduction method. Since the traditional maximum likelihood approach for standard GP cannot be applied to our model, we adopt a variational inference method to solve the GPLVMF model. As a result, we improve the results on the real datasets significantly as well as capturing the importance of each context. In addition to the general advantages, our method flexibly provides two contributions regarding recommender system settings: (1) addressing the influence of bias by setting a non-zero mean function, and (2) utilizing real-valued contexts by fixing the latent space with real values.