ID3P: Iterative Data-Driven Development of Persona based on Quantitative Evaluation and Revision

被引：11

作者：

Watanabe, Yasuhiro ^{[1
]}

Washizaki, Hironori ^{[1
]}

Honda, Kiyoshi ^{[1
]}

Noyori, Yuki ^{[1
]}

Fukazawa, Yoshiaki ^{[1
]}

Morizuki, Aoi ^{[2
]}

Shibata, Hiroyuki ^{[2
]}

Ogawa, Kentaro ^{[2
]}

Ishigaki, Mikako ^{[2
]}

Shiizaki, Satiyo ^{[2
]}

Yamaguchi, Teppei ^{[2
]}

Yagi, Tomoaki ^{[2
]}

机构：

[1] Waseda Univ, Tokyo, Japan

[2] Yahoo Japan Corp, Tokyo, Japan

来源：

2017 IEEE/ACM 10TH INTERNATIONAL WORKSHOP ON COOPERATIVE AND HUMAN ASPECTS OF SOFTWARE ENGINEERING (CHASE 2017) | 2017年

关键词：

Requirements engineering; Data analysis; Consumer behavior; Personas; GQM plus Strategies;

D O I：

10.1109/CHASE.2017.9

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Personas are fictional characters used to understand users' requirements. Many researchers have proposed persona development methods from quantitative data (data-driven personas development). However, it is not assumed that personas in these works are used continuously and these personas cannot reflect on unpredictable changes in users. It is difficult to plan reliable strategies in a web service because users' preference dynamically changes. To develop more suitable personas for decision-making in a web service, this paper proposes Iterative Data-Driven Development of Personas (ID3P). In particular, to detect an unpredictable change in users' characteristics, our proposal includes an iterative process where the personas are quantitatively evaluated and revised in each iteration. Moreover, it provides a quantitative evaluation of business strategies based on GQM+Strategies and personas. To verify our proposal, we applied it to Yahoo!JAPAN's web service called Netallica.

引用

页码：49 / 55

页数：7

共 50 条

[21] The evaluation and optimization of the agricultural sustainable development based on a data-driven approach: A case from
Gao, Fengwei
Li, Zhuangzhuang
Zhang, Pei
Wu, Yimin
HELIYON, 2024, 10 (12)
[22] Development and evaluation of data-driven controls for residential smart thermostats
Huchuk, Brent
Sanner, Scott
O'Brien, William
ENERGY AND BUILDINGS, 2021, 249
[23] Intelligent Data-Driven Decision Support for Agricultural Systems-ID3SAS
Araujo, Sara Oleiro
Peres, Ricardo Silva
Filipe, Leandro
Manta-Costa, Alexandre
Lidon, Fernando
Ramalho, Jose Cochicho
Barata, Jose
IEEE ACCESS, 2023, 11 : 115798 - 115815
[24] Data-driven Adaptive Iterative Learning Control Based on a Local Dynamic Linearization
Zhang, Shuhua
Hui, Yu
Chi, Ronghu
PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS), 2018, : 184 - 188
[25] Event-Triggered Based Data-Driven Predictive Iterative Learning Control
Yu, Qiongxia
Fan, Zhihao
Tian, Fengchen
Hou, Zhongsheng
2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS, 2023, : 1907 - 1913
[26] An Identification Based Indirect Iterative Learning Control via Data-driven Approach
Chi, Ronghu
Su, Tao
Jin, Shangtai
2012 12TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS & VISION (ICARCV), 2012, : 1773 - 1776
[27] A learning algorithm for a Data-driven Controller based on Fictitious Reference Iterative Tuning
Wakitani, Shin
Yamamoto, Tofu
2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 4908 - 4913
[28] An iterative data-driven turbulence modeling framework based on Reynolds stress representation
Yuhui Yin
Zhi Shen
Yufei Zhang
Haixin Chen
Song Fu
Theoretical & Applied Mechanics Letters, 2022, 12 (05) : 371 - 387
[29] An iterative data-driven turbulence modeling framework based on Reynolds stress representation
Yin, Yuhui
Shen, Zhi
Zhang, Yufei
Chen, Haixin
Fu, Song
THEORETICAL AND APPLIED MECHANICS LETTERS, 2022, 12 (05)
[30] A Data-Driven Iterative Learning Control Framework Based on Controller Dynamic Linearization
Hou, Zhongsheng
Yu, Xian
Yin, Chenkun
2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC), 2018, : 5588 - 5593

← 1 2 3 4 5 →