Abstract: Sequential recommendation systems aim to predict users’ next actions based on the preferences learned from their historical behaviors. There are still fundamental challenges for sequential recommender. First, with the popularization of online services, recommender need serve both the warm-start and cold-start users. However, the most existing models depending on user-item interactions lose merits due to the difficulty of learning sequential dependencies with limited interactions. Second, users’ behaviors in their historical sequences are often implicit and complex due to the objective variability of reality and the subjective randomness of users’ intentions. It is difficult to capture the dynamic transition patterns from these user-item interactions. In this work, we propose a graph-based interpolation enhanced sequential recommender with deformable convolutional network (GISDCN). For cold-start users, we reconstruct item sequences into a graph to infer users’ possible preferences. To capture the complex sequential dependencies, we employ the deformable convolutional network to generate more robust and flexible filters. Finally, we conduct comprehensive experiments and verify the effectiveness of our model. The experimental results demonstrate that GISDCN outperforms most of the state-of-the-art models at cold-start conditions.