Personalized recommendation for online service systems aims to predict potential demand by analysing user preference. User preference can be inferred from heterogeneous implicit feedback (i.e. various user actions) especially when explicit feedback (i.e. ratings) is not available. However, most methods either merely focus on homogeneous implicit feedback (i.e. target action), e.g., purchase in shopping websites and forward in Twitter, or dispose heterogeneous implicit feedback without the investigation of its speciality. In this paper, we adopt two typical actions in online service systems, i.e., view and like , as auxiliary feedback to enhance recommendation performance, whereby we propose a Bayesian personalized ranking method for heterogeneous implicit feedback (BPRH). Specifically, items are first classified into different types according to the actions they received. Then by analysing the co-occurrence of different types of actions, which is one of the fundamental speciality of heterogeneous implicit feedback systems, we quantify their correlations, based on which the difference of users’ preference among different types of items is investigated. An adaptive sampling strategy is also proposed to tackle the unbalanced correlation among different actions. Extensive experimentation on three real-world datasets demonstrates that our approach significantly outperforms state-of-the-art algorithms.