In recommender system field， most of the existing works mainly focus on the One-Class Collaborative Filtering （OCCF） problem with only one type of users’ feedback， e.g.， purchasing feedback. However， users’ feedback is usually heterogeneous in real applications， so it has become a new challenge to model the users’ heterogeneous feedback to capture their true preferences. Focusing on the Heterogeneous One-Class Collaborative Filtering （HOCCF） problem （including users’ purchasing feedback and browsing feedback）， a transfer learning solution named Staged Variational AutoEncoder （SVAE） model was proposed. Firstly， the latent feature vectors were generated via the Multinomial Variational AutoEncoder （Multi-VAE） with users’ browsing feedback auxiliary data. Then， the obtained latent feature vectors were transferred to another Multi-VAE to assist the modeling of users’ target data， i.e.， purchasing feedback by this Multi-VAE. Experimental results on three real-world datasets show that the performance of SVAE model on the important metrics such as Precision@5 and Normalized Discounted Cumulative Gain@5 （NDCG@5） is significantly better than the performance of the state-of-the-art recommendation algorithms in most cases， demonstrating the effectiveness of the proposed model.