In recent years, with the popularity of mobile smart devices, location based social networks are on the rise. Users trend to share their wonderful experiences with their friends, resulting in producing large-scale user trajectory with temporal and spatial attributes. From a narrow perspective, the trajectory data refers to continuously sampled GPS data only. From a broad perspective, it can be called trajectory data as long as the data has sequential characteristic. Thus, the check-ins, acquired from a social network, can also be considered coarse-grained trajectory data. The generalized trajectory data has the characteristics of spatiotemporal heterogeneity, continuous and discrete coexistence, and containing temporal-spatial items with unclear hierarchy and classification. However, compared to the GPS trajectory data, the generalized trajectory data source is extensive and contains rich information, which brings great opportunity to the traditional mobile recommender system. At the same time, the generalized trajectory data has big scale and diversity structure, which also presents great challenges to the system. It has become an important issue how to use the generalized trajectory data to improve the performance of mobile recommender system in academia and industry. This paper takes the trajectory data characteristics as the focal point to analyze and survey main recommender methods and evaluation metrics based on generalized user trajectory data. Further, it expounds the relationships and differences between traditional mobile recommender systems and the mobile recommender systems based on user trajectory data. Finally, the paper discusses the difficulty and development trend of mobile recommender systems based on generalized user trajectory.