Abstract:The recommendation algorithm is one of the most important algorithms in data mining. Location recommendation is an important research content of the recommendation system. Aiming at the problems of sparse data, cold start and low degree of personalization, the improved hybrid location recommendation algorithm based on Spark parallelization is designed and implemented. The algorithm combines content-based recommendations and collaborative filtering-based recommendations, combines the user's current preferences with the opinions of other users. We improve data sparsity by using a matrix fill based on user preferences for location attributes; Also, for massive data, the system uses Spark distributed cluster to realize parallel computing, which shortens the model training time. Experimental results show that compared with other recommended algorithms, the proposed algorithm can effectively improve data sparsity and improve recommendation.