Travel route recommendation for Location-based Social Networks (LBSNs) has been received much attention to research people’s activity patterns and personalized preferences. Existing travel route recommendation schemes in literature are confronted with three problems: 1) the location is limited in practical environment, and the data sparsity is always happened when they recommend travel route services based on the location information; 2) they fail to consider the order of mobile trajectory, which is valuable to reflect the interest and preference of users for travel route recommendation; 3) they can’t be adapted to different kinds of POI category, which causes the extendibility is low. In this paper, we propose PP-TRR, a pattern and preference-aware travel route recommendation scheme to tackle the above problems. First, we construct the system architecture of our proposed travel route recommendation. Then, we model the movement pattern of each user. Finally, we present the travel route recommendation scheme to recommend personalized services for targeted users. The experimental results show that our method outperforms the existing method.