This paper proposes a novel recommendation service to help visitors to find their proper automobile exhibition halls for auto show. In the proposed method, both temporal and spatial features of visitors are first considered to construct their profiling, and then extract their interests based on visitors' clustering. Finally, highly desired exhibition halls are personalized recommended to proper visitor. The proposed recommender system consists of three modules including relevance module, quality module and integration module. The relevance module is developed to measure the relationship of an automobile exhibition and a visitor, while the quality module is constructed to analyze the quality of each automobile exhibition. The integration module is to combine two modules above for appropriate automobile exhibition. The proposed approach is well validated using a real world dataset, and compared with several baseline models. Our experimental results indicate that in terms of the well-known evaluation metrics, the proposed method can achieves more useful and feasible recommendation results, and our finding highlights that the proposed method can help both visitors to find a more appropriate automobile exhibition halls, and manage officers to reduce more management cost.