关键词: 位置预测, 簇标记转移矩阵, 用户移动性, 向量自回归, 簇标记转移向量序列

Abstract: The key defects of existing user mobility discovery methods, such as PMM (periodic mobility model), W3 etc., are the lack of the capability to objectively and quantitatively reflect the effect of time factor on the users location change and geographical relationship, which makes it difficult to accurately predict the user's future check-in location. To solve this problem, this paper proposes a location transfer evolution algorithm based on vector auto-regressive (LTE), which takes in the large volume real user check-in location history as training datasets and learns the regularity of users location transfer over the change of time. The extensive experiments on real check-in datasets of Foursquare and Gowalla are conducted, which shows that compared with W3, LTE (the proposed algorithm)     improves the prediction accuracy by 4.43%—21.31%, and compared with PMM, LTE improves the prediction accuracy by 25.07%—38.50%.

Key words: location prediction, cluster marker transfer matrix, user mobility, vector autoregressive, cluster marker transfer vector sequence