A Low-Cost High-Performance Semantic and Physical Distance Calculation Method Based on ZIP Code
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- LI Da
- Kyoto Sangyo University
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- WANG Yuanyuan
- Yamaguchi University
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- YAMAMOTO Rikuya
- Kyoto Sangyo University
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- KAWAI Yukiko
- Kyoto Sangyo University Osaka University
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- SUMIYA Kazutoshi
- Kwansei Gakuin University
説明
<p>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 high-dimensional vector calculation. Thus, a low-cost user behavior and user movement history analysis approach should be considered. To address this issue, firstly, 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 zip-code-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.</p>
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E105.D (5), 920-927, 2022-05-01
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390010457646376576
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- ISSN
- 17451361
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- KAKEN
- OpenAIRE
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- 抄録ライセンスフラグ
- 使用不可