Internet recommender systems are popular in contexts that include heterogeneous consumers and numerous products. In such contexts, product features that adequately describe all the products are often not readily available. Content-based systems therefore rely on user-generated content such as product reviews or textual product tags to make recommendations. In this paper, we develop a novel covariate-guided, heterogeneous supervised topic model that uses product covariates, user ratings, and product tags to succinctly characterize products in terms of latent topics and specifies consumer preferences via these topics. Recommendation contexts also generate big-data problems stemming from data volume, variety, and veracity, as in our setting, which includes massive textual and numerical data. We therefore develop a novel stochastic variational Bayesian framework to achieve fast, scalable, and accurate estimation in such big-data settings and apply it to a MovieLens data set of movie ratings and semantic tags. We show that our model yields interesting insights about movie preferences and makes much better predictions than a benchmark model that uses only product covariates. We show how our model can be used to target recommendations to particular users and illustrate its use in generating personalized search rankings of relevant products.