Tag-aware recommendation based on Bayesian personalized ranking and feature mapping

Collaborative filtering recommendation with implicit feedbacks (i.e., clicks, views, check-ins) has been gaining increasing attention in various real applications. Tagging information is the common resource to complement implicit feedbacks to assist collaborative filtering recommendation. However, existing tag-aware recommendation methods still suffer from the problem of high dimension and sparsity of tagging information. They also fail to realize that recommendation is inherent a ranking-oriented optimization task. To this end, we propose a novel tag-aware recommendation framework by incorporating tag mapping scheme into ranking-based collaborative filtering model, to boost ranking-oriented personalized recommendation performance. We first build ranking-oriented optimization model based on Bayesian personalized ranking optimization criterion with matrix factorization, by leveraging implicit feedbacks to learn the latent feature vectors of users and items. Then, we propose an explicit-to-implicit feature mapping scheme, mapping the high-dimensional and sparse explicit tags (i.e., user-tag weighting matrix and item-tag weighting matrix) to low-dimensional and compact implicit features of uses and items. This could serve as the regularization constraint of latent features derived from implicit feedbacks. To further enhance recommendation performance, we also introduce users' neighbor relationships to regularize user latent features based on manifold learning. Experiments on real-world recommendation datasets show that the proposed recommendation method outperformed competing methods on ranking-oriented recommendation performance.