Structured Sparse Regression for Recommender Systems.

Feature-based collaborative filtering models, such as state-of-the-art factorization machines and regression-based latent factor models, rarely consider features' structural information, ignoring the heterogeneity of inter-type and intra-type relationships. Naïvely treating all feature pairs equally would potentially deteriorate the overall recommendation performance. In addition, human prior knowledge and other hierarchical or graphical structures are often available for some features, e.g., the country-state-city hierarchy for geographic features and the topical taxonomy for article features. It is a challenge to utilize the prior knowledge to further boost performance of state-of-the-art models. In this paper we employ rich features from both user and item sides to enhance latent factors learnt from interaction data, uncovering hidden structures from features' relationships and learning sparse pairwise and tree structural connections among features. Our framework borrows the modeling strengh from both structural sparsity modeling and latent factor models. Experiments on a real-world large-scale recommendation data set demonstrated that the proposed model outperforms several strong state-of-the-art baselines.