著者

松林 達史 幸島 匡宏 林 亜紀 澤田 宏

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.30, no.6, pp.713-720, 2015-11-01 (Released:2015-10-27)

参考文献数

22

被引用文献数

6 7

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In marketing science field, modeling of purchase behavior and analysis of brand choice are important research tasks. This paper presents a method that enables such analysis by time-series pattern extraction based on Non-negative Tensor Factorization (NTF). The development of the scanning devices and electronic payments (e.g. online shopping, mobile-phone wallet and electronic money) has led to the accumulation of more detailed POS data including the information about purchase shop, amount of payment, time, location and so on and it brings possibilities for more deep understanding of purchasing behaviors. On the other hand, due to the increase of the number of attributes, it is still difficult to effectively and efficiently handle large feature quantities. In this paper, we consider feature quantities as high-order tensor. Then, using NTF for simultaneous decomposition of multiple attributes, we show analytic effectiveness of pattern factorization for real Beer Item/Brand purchase data. By applying NTF considering three axes: USER-ID × TIME-STAMP × ITEM-ID,we find several temporal tendencies depending on the season.In addition, by focusing on the purchase-pattern correlations between beer items and brands, we find that the tendencies of brand choice strategies appear on the graph drawing results.

著者

幸島 匡宏 松林 達史 澤田 宏

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.30, no.6, pp.745-754, 2015-11-01 (Released:2015-10-27)

参考文献数

18

被引用文献数

4

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In this paper, we propose a new Non-negative Matrix Factorization (NMF) method for consumer behavior pattern extraction. NMF is one of the pattern extraction method and is formulated to factorize a non-negative matrix into the product of two factor matrices. Since various types of datasets are represented by non-negative matrices, NMF could be applied in wide range of research fields including marketing science, natural language processing and brain signal processing. However, more effective extension method is required in a purchase log analysis in marketing operation since marketer needs to extract interpretable patterns from sparse matrix in which most of the elements are zero. Therefore, we propose Non-negative Micro Macro Mixed Matrix Factorization (NM4F) which uses attribution information of both users and items to improve interpretability and capability to deal with sparsity. NM4F is formulated as a method which could simultaneously factorize multiple matrices using shared factor matrices and linear constraint between factor matrices. This formulation enables to increase an amount of available information and to extract consistent patterns with several different aspect. We derive the parameter estimation algorithm by multiplicative update rules. We confirmed the effectiveness of the proposed method in terms of both quality and quantity by using real consumer panel dataset. In addition, we discuss a relation between extracted patterns by the visualization results using graph drawing.

著者

松林 達史 清武 寛 幸島 匡宏 戸田 浩之 田中 悠介 六藤 雄一 塩原 寿子 宮本 勝 清水 仁 大塚 琢馬 岩田 具治 澤田 宏 納谷 太 上田 修功

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.34, no.5, pp.wd-F_1-11, 2019-09-01 (Released:2019-09-01)

参考文献数

29

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Forming security plans for crowd navigation is essential to ensure safety management at large-scale events. The Multi Agent Simulator (MAS) is widely used for preparing security plans that will guide responses to sudden and unexpected accidents at large events. For forming security plans, it is necessary that we simulate crowd behaviors which reflects the real world situations. However, the crowd behavior situations require the OD information (departure time, place of Origin, and Destination) of each agent. Moreover, from the viewpoint of protection of personal information, it is difficult to observe the whole trajectories of all pedestrians around the event area. Therefore, the OD information should be estimated from the several observed data which is counted the number of passed people at the fixed points.In this paper, we propose a new method for estimating the OD information which has following two features. Firstly, by using Bayesian optimization (BO) which is widely used to find optimal hyper parameters in the machine learning fields, the OD information are estimated efficiently. Secondly, by dividing the time window and considering the time delay due to observation points that are separated, we propose a more accurate objective function.We experiment the proposed method to the projection-mapping event (YOYOGI CANDLE 2020), and evaluate the reproduction of the people flow on MAS. We also show an example of the processing for making a guidance plan to reduce crowd congestion by using MAS.

著者

幸島 匡宏 松林 達史 澤田 宏

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

電子情報通信学会論文誌 D (ISSN:18804535)

巻号頁・発行日

vol.J100-D, no.4, pp.520-529, 2017-04-01

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近年,網羅的なデータ収集の困難さや個人情報保護の観点から,ユーザ個人を単位とする粒度の細かいデータと年代・性別などの属性情報でまとめられたユーザ集団を単位とする粒度の粗いデータといった,異なる粒度のデータを扱う機会が増えている.本研究では,個人単位・集団単位のデータの組のように,粒度の異なる複数のデータを組み合わせて分析するための新たな手法を提案する.提案手法は,非負値行列分解に基づく新たな確率モデルである.このモデルは粒度の粗い方のデータの背後に存在するデータを潜在変数として導入することで導かれる.実購買履歴を用いた実験を通して,提案手法は既存手法を上回る性能を示したこと,低解像度のデータ数が増えるに従い性能向上が達成されることを確認した.更に上記提案手法に基づく拡張手法を導出することで,提案手法が様々な異粒度データ分析問題を考える際の基盤的アプローチとなりうることを示す.

著者

清武 寛 幸島 匡宏 松林 達史 戸田 浩之

出版者

人工知能学会

雑誌

人工知能学会全国大会論文集 (ISSN:13479881)

巻号頁・発行日

vol.31, 2017

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一般道での交通渋滞やテーマパークにおける長い待ち時間など,いわゆる混雑が問題視されている. それ故,交通円滑化や待ち時間削減のために,信号制御や入場制限などの制御による対策が行われている. 一般的に,制御策の検討にはマルチエージェントシミュレータ(MAS)が用いられるが,主に経験則に基づいた制御策による試行が行われている. 本論文では,自動的に最適な制御策を探索する手法を提案する.