It is critical to comprehensively and efficiently learn user preferences for an effective sequential recommender system. Existing sequential recommendation methods mainly focus on modeling local preference from users’ historical behaviors, which largely ignore the global context information from the heterogeneous information network. This prevents a comprehensive user preference representation. To address these issues, we propose a joint learning approach to incorporate global context with local preferences efficiently. The proposed approach introduces meta paths from a heterogeneous information network to capture the global context information, and the position-based self-attention mechanism is adopted to model the local preference representation efficiently. Compared with the methods that only consider the local preference, our proposed method takes the advantages of incorporating global context information, which extracts structural features that captures relevant semantics to construct users’ global preference representation for the sequential recommendation. We further adopt a co-attention mechanism to model complex interactions between global context and users’ historical behaviors for better user representations. Quantitative and qualitative experimental evaluations are conducted on nine large-scale Amazon datasets and a multi-modal Zhihu dataset. The promising results demonstrate the effectiveness of the proposed model.