A Low-Cost High-Performance Semantic and Physical Distance Calculation Method Based on ZIP Code

Da LI, Yuanyuan WANG, Rikuya YAMAMOTO, Yukiko KAWAI, Kazutoshi SUMIYA
2022 IEICE transactions on information and systems  
Recently, machine learning approaches and user movement history analysis on mobile devices have attracted much attention. Generally, we need to apply text data into the word embedding tool for acquiring word vectors as the preprocessing of machine learning approaches. However, it is difficult for mobile devices to afford the huge cost of highdimensional vector calculation. Thus, a low-cost user behavior and user movement history analysis approach should be considered. To address this issue,
more » ... tly, we convert the zip code and street house number into vectors instead of textual address information to reduce the cost of spatial vector calculation. Secondly, we propose a low-cost high-performance semantic and physical distance (real distance) calculation method that applied zipcode-based vectors. Finally, to verify the validity of our proposed method, we utilize the US zip code data to calculate both semantic and physical distances and compare their results with the previous method. The experimental results showed that our proposed method could significantly improve the performance of distance calculation and effectively control the cost to a low level.
doi:10.1587/transinf.2021dap0005 fatcat:ptujwrsiwvguxdkjc4pjp3dpbi