Maximizing sales and revenue is an important goal of online commercial retailers. Recommender systems are designed to maximize users' click or purchase probability, but often disregard users' eventual satisfaction with purchased items. As result, such systems promote items with high appeal at the selling stage (e.g. an eye-catching presentation) over items that would yield more satisfaction to users in the long run. This work presents a novel unified model that considers both goals and can be tuned to balance between them according to the needs of the business scenario. We propose a multi-task probabilistic matrix factorization model with a dual task objective: predicting binary purchase/no purchase variables combined with predicting continuous satisfaction scores. Model parameters are optimized using Variational Bayes which allows learning a posterior distribution over model parameters. This model allows making predictions that balance the two goals of maximizing the probability for an immediate purchase and maximizing user satisfaction and engagement down the line. These goals lie at the heart of most commercial recommendation scenarios and enabling their balance has the potential to improve value for millions of users worldwide. Finally, we present experimental evaluation on different types of consumer retail datasets that demonstrate the benefits of the model over popular baselines on a number of well-known ranking metrics.