Different from the traditional recommender systems with content-based and collaborative filtering, conversational recommender systems (CRS) can dynamically dialogue with users to capture fine-grained preferences. Although several efforts have been made for CRS, they neglect the importance of interaction sequences, which seek to capture the ‘context’ of users’ activities based on actions they have performed recently. Therefore, we propose a framework that considers interaction Sequence of historical items for Conversational Recommendation (SeqCR). Specifically, SeqCR first scores candidate items through the sequence which users interact with. Then it can generate the recommendation list and attributes to be asked based on the scores. We restrict candidate attributes to the ones with high-scoring (high-relevance) items, which effectively reduces the search space of attributes and leads to user preferences that can be hit more quickly and accurately. Finally, SeqCR utilizes the policy network to decide whether to recommend or ask. We conduct extensive experiments on two datasets from MovieLens 10M and Yelp in multi-round conversational recommendation scenarios. Empirical results demonstrate our SeqCR significantly outperforms the state-of-the-art methods.