Leverage side information for top-N recommendation with latent Gaussian process

Currently, Recommender Systems (RS) have been ubiquitously applied to various online applications and obtained tremendous success due to their capability to overcome information overload; however, the available Side Information (SI), such as demographics and attributions of items, is always neglected. Actually, SI could reflect user's interests and preference, and even influence user's decision over items; therefore, it would be greatly helpful to leverage side information to improve the performance of RS, which is already partly certificated in previous research studies. Motivated by this, in this article, a novel multi-task learning-based recommendation system referred to as LGP-RS is proposed, which takes utility functions mapping from the non-linear side information to low-rank feature space with Gaussian process (GP) priors, for users and items, respectively. In addition, Laplace approximation method is employed to approximate the posterior distribution for the utility functions, and gradient-based method is employed to learn the hyperparameters for GP. This flexible approach is able to capture the non-linear high-order interactions within side information, reduce the prediction uncertainty, and provide personalized top-N recommendation. Experimental analysis over three real-world datasets demonstrates that LGP-RS could deal with both explicit and implicit side information simultaneously, and significantly outperforms state-of-the-art approaches.