Since the traditional collaborative filtering algorithm cannot make full use of information implied in check-ins of users in recommendation process, which contains users' preference, location and social relationship, a recommendation algorithm was proposed, which exploits past user behavior, the check-in information and social relation of users to improve the precision of Point of Interests (POI) recommendation, namely Location-Friendship Based Collaborative Filtering (LFBCF). And the recommendation was implemented on distributed computing platform Spark to support large scale dataset in experiments. Two real datasets in Location-based Social Network (LBSN) including Gowalla and Brightkite were employed in experiments. The amount of check-ins, the distance between locations and the social relationship were analyzed to verify the proposed algorithm. The comparison of precision and F-measure with traditional algorithm confirms the effectiveness of the proposed algorithm; and the comparison of speed-up ratio between the parallelized algorithm and serial algorithm demonstrates the significance of parallelization and superiority of performance.