2017 年 32 巻 1 号 p. WII-H_1-10
Understanding of human behavior patterns is important for many tasks such as location-based recommendation, urban design and crowd control. Our purpose is to extract easy-to-grasp multiple transition patterns which have hierarchical periodicities (e.g., weekly, daily) for enhancing above mentioned tasks. Conventional Hidden Markov Model extracts typical transition patterns, though extracted patterns are not always based on explicit periodicities and extracted patterns are independent. Also, even though conventional Nonnegative Matrix Factorization (NMF) approximates human behaviors as the weighted sum of multiple typical visit frequency distributions, it is not able to extract transition patterns. The proposed method solves these problems by extending a kind of extension of NMF called NMF with Markov-chained bases in two perspectives by approximating human behaviors as the weighted sum of multiple typical transition patterns in hierarchical periods. First, the proposed method extracts patterns interpolating missing values that are typical for location history. Second, we add restriction for estimating transition patterns that promotes extraction of transition patterns in hierarchical periods. For example, the proposed method can approximate a user’s behavior as the weighted sum of daily transition pattern on working day (e.g. leave home and go to the office in the morning, go to the restaurant and return to the office in lunch break and return home in the evening) and weekly transition pattern (e.g., visit office in week day and go to the other recreation places in weekends).