Based on explicit data, collaborative filtering is one of the most valuable technologies of a recommender system. However, the further development of a recommender system has been restricted to some extent by the problems of cold start and data sparsity. To weaken the effect caused by data loss, some implicit feedback data are typically introduced into the recommendation such as social information and attribute data. In this paper, we propose a heterogeneous information boosting model for recommendations (HIBoosting). The model takes advantage of different network meta-paths to mine the potential information of user social networks, object metadata and interactive data in heterogeneous information networks. For different semantic information, we use a random-walk based on a meta-path to calculate the similarities of users or items, which are used to extract the low-dimensional embedded features of users or items by factorization, construct user profiles and item profiles. Finally, the embedded features are used for integrated learning by a gradient boosting decision tree (GBDT) to provide users with better recommendation services. The model not only integrates heterogeneous information in information networks, but also makes full use of mining latent relations of heterogeneous information networks. Experiments on the CiaoDVD and Lastfm datasets show that HIBoosting has made great progress in the accuracy of a recommender system.