518 research outputs found
BERT, SHAP, Kano ๋ชจ๋ธ์ ๊ธฐ๋ฐํ ์๋น์ ๋ง์กฑ ์์ ๋ค์ด๋๋ฏน์ค
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ฒฝ์๋ํ ๊ฒฝ์ํ๊ณผ, 2022.2. ์ค์ ์ ๊ต์.์ต๊ทผ 10๋
๊ฐ ์จ๋ผ์ธ ์ผํ ์ฐ์
์ ์ฑ์ฅ์ผ๋ก ์จ๋ผ์ธ ์ผํ๋ชฐ ํ๋ซํผ์ ์จ๋ผ์ธ ๋ฆฌ๋ทฐ ๋ฑ ๋ฌดํํ ์๋น์ ๋ฐ์, ๋ง์กฑ๋ ๊ด๋ จ ์ ๋ณด๊ฐ ์์ฑ๋๊ณ ์๋ค. ์ด์ ๋ง์ ๊ธฐ์
๋ค๊ณผ ํ๊ณ์์ ์ด๋ฅผ ํ์ฉํ์ฌ VoC (Voice of Customer)๋ฅผ ๋ฐ์ํ ์๋น์ ๋ง์กฑ๋ ๋ชจ๋ธ๋ง์ ์๋ํ๊ณ ์๋ค. ๋ณธ ๋
ผ๋ฌธ์ BERT, GBM, SHAP ๋ฑ์ ํ์ฉํ์ฌ ์นด๋
ธ ๋ชจ๋ธ (Kano Model)์ ๊ธฐ๋ฐํ ์๋น์ ๋ง์กฑ๋ ํน์ฑ (Customer Satisfaction Dimension)์ ๋ถ๋ฅํ๊ณ ๊ฐ ํน์ฑ์ ์๋น์ ์๊ตฌ ์ถฉ์กฑ ์ฌ๋ถ๊ฐ ์๋น์ ๋ง์กฑ๋์ ๋ฏธ์น๋ ์ํฅ๋๋ฅผ ์ธก์ ํ๋ค. ๋ณธ ๋
ผ๋ฌธ์ ๋ฐฉ๋ฒ๋ก ์ ํ์ฉ๋ ๊ฐ ๋น
๋ฐ์ดํฐ ๋ชจ๋ธ ์ฑ๋ฅ๊ณผ ์ ํ ์ฐ๊ตฌ๋ค์์ ์ฌ์ฉ๋ ๋ชจ๋ธ ์ฑ๋ฅ์ ์ง์ ๊ตฌํ ๋ฐ ๋น๊ตํ์ฌ, ๋ณธ ๋
ผ๋ฌธ์์ ํ์ฉ๋ ๋ชจ๋ธ๋ค์ ์ ํ์ฑ๊ณผ ์์ ์ฑ์ ๋ณด์๋ค. ๋ํ ํด์์ ๋จธ์ ๋ฌ๋ ๊ธฐ๋ฒ์ธ SHAP๋ฅผ ๋์
ํ์ฌ, ์นด๋
ธ ์นดํ
๊ณ ๋ฆฌ๋ฅผ ๋ถ๋ฅํ๋ ํต์ผ๋ ๋ถ๋ฅ ๋ฐฉ์์ ์ ์ํ๋ค. ๋ณธ ์ฐ๊ตฌ๋ ์ ์๋ ๋ฐฉ๋ฒ๋ก ์ ํตํด ์ค๋งํธํฐ ๋ฐ ์ค๋งํธ์์น ์ ํ๊ตฐ์ ๋์์ผ๋ก ์ค์ฆ ์ฐ๊ตฌ๋ฅผ ์งํํ๋ฉฐ, ์ฐ์
๊ณ์ ์ ํ ๊ฐ๋ฐ ๋ฐ ๊ฐ์ , ๊ณ ๊ฐ ์ธ๋ถํ ์ ๋ต ๋ฑ ๊ธฐ์
์์ฌ๊ฒฐ์ ๋ฐฉํฅ์ฑ์ ์ ์๋ฏธํ ์ ์ธ์ ์ ์ํจ์ผ๋ก์จ ๋ณธ ๋ฐฉ๋ฒ๋ก ์ ์ค์ฉ์ ๊ฐ์น๋ฅผ ์
์ฆํ์๋ค.As a large number of online reviews are loaded on e-commerce platforms in recent days, companies are being able to measure customer satisfaction reflecting VoC (Voice of Customer) with big data analytics. This paper proposes the improved framework for identifying characteristics of customer satisfaction dimensions (CSD) based on Kano model using BERT (Bidirectional Encoder Representations from Transformers), GBM (Gradient Boosting Machine), and SHAP (Shapley Additive eXplanation). We proved each model outperformance by comparing other models which previous studies have used. And this paper suggests the unified rule of Kano model classification using SHAP. Furthermore, we conducted empirical studies regarding smartphone and smartwatch products which suggest the direction of product enhancement/development strategy and multi-product level customer segmentation strategy to product manufacturers. This shows proposed methodologyโs effectiveness and usefulness on industrial analysis.1. Introduction 1
2. A framework for modelling customer satisfaction from online review 5
3. Research Method 8
3.1 Mining customerโs sentiments toward CSDs from online reviews 8
3.2 Measuring the effects of customer sentiments toward each CSD on customer satisfaction 11
3.3 Identifying the feature of each CSD from the customerโs perspective 11
3.4 Classifying each CSD into Kano categories 14
4. Empirical Study 17
4.1 Study 1 17
4.2 Study 2 24
5. Conclusion 27
6. Reference 29์
Visual analytics for supply network management: system design and evaluation
We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip
Redrawing the 2012 map of the Maryland congressional districts
Gerrymandering is the practice of drawing biased electoral maps that
manipulate the voter population to gain an advantage. The most recent time
gerrymandering became an issue was 2019 when the U.S. Federal Supreme Court
decided that the court does not have the authority to dictate how to draw the
district map and state legislators are the ones who should come up with an
electoral district plan. We solve the political districting problem and redraw
the 2012 map of Maryland congressional districts which raised the issue in
2019.Comment: 8 pages, to be submitted to IISE 2024 Annual Conference Proceeding
When to Signal? The Contextual Conditions for Career-Motivated User Contributions in Online Collaboration Communities
This paper examines the contextual conditions for usersโ career concern as a motivational driver of contributions in online collaboration communities. On the data of user-level activities from a computer programming-related online Q&A community (Stack Overflow), merged with job-market data for software-developer, we find robust evidence of a positive association between individual usersโ career concern and their contributions. More important, we find that this positive relationship is further strengthened through the contextual conditions: the number of vacancies in the job market, the expected salaries from these jobs, and the transparency in the flow of career-related information within the community. We contribute to the literature on motivation in online collaboration communities. Our study thus offers insight into how career concern can be effectively utilized to motivate contributors in these communities. Our findings also foreshadow a possible paradigm change by characterizing online collaboration communities as institutions of career concern and skill signaling
When to Signal? Contingencies for Career-Motivated Contributions in Online Collaboration Communities
Online collaboration communities are increasingly taking on new roles beyond knowledge creation and exchange, especially the role of a skill-signaling channel for career-motivated community members. This paper examines the contingency effects of job-market conditions for career-motivated knowledge contributions in online collaboration communities. From the data of individual-level activities in a computer programming-related online Q&A community (Stack Overflow), merged with job-market data for software developers, we find robust evidence of a positive association between community membersโ career motivations and their knowledge contributions. More importantly, we find that this positive relationship is strengthened by job-market conditions: the number of vacancies in the job market, the expected salaries from these jobs, and the transparency in the flow of career-related information between the community and external recruiters. We contribute to the motivation literature in online collaboration communities by identifying and substantiating the role of contextual factors in mobilizing membersโ career motivation. Our study thus offers novel insight into how career motivation can be effectively utilized to motivate contributors in these communities. Our findings also point to a possible paradigm change by characterizing online collaboration communities as emerging institutions for career motivation and skill signaling
Prompt Learning via Meta-Regularization
Pre-trained vision-language models have shown impressive success on various
computer vision tasks with their zero-shot generalizability. Recently, prompt
learning approaches have been explored to efficiently and effectively adapt the
vision-language models to a variety of downstream tasks. However, most existing
prompt learning methods suffer from task overfitting since the general
knowledge of the pre-trained vision language models is forgotten while the
prompts are finetuned on a small data set from a specific target task. To
address this issue, we propose a Prompt Meta-Regularization (ProMetaR) to
improve the generalizability of prompt learning for vision-language models.
Specifically, ProMetaR meta-learns both the regularizer and the soft prompts to
harness the task-specific knowledge from the downstream tasks and task-agnostic
general knowledge from the vision-language models. Further, ProMetaR augments
the task to generate multiple virtual tasks to alleviate the meta-overfitting.
In addition, we provide the analysis to comprehend how ProMetaR improves the
generalizability of prompt tuning in the perspective of the gradient alignment.
Our extensive experiments demonstrate that our ProMetaR improves the
generalizability of conventional prompt learning methods under
base-to-base/base-to-new and domain generalization settings. The code of
ProMetaR is available at https://github.com/mlvlab/ProMetaR.Comment: CVPR 202
Deep Coherence Learning: An Unsupervised Deep Beamformer for High Quality Single Plane Wave Imaging in Medical Ultrasound
Plane wave imaging (PWI) in medical ultrasound is becoming an important
reconstruction method with high frame rates and new clinical applications.
Recently, single PWI based on deep learning (DL) has been studied to overcome
lowered frame rates of traditional PWI with multiple PW transmissions. However,
due to the lack of appropriate ground truth images, DL-based PWI still remains
challenging for performance improvements. To address this issue, in this paper,
we propose a new unsupervised learning approach, i.e., deep coherence learning
(DCL)-based DL beamformer (DL-DCL), for high-quality single PWI. In DL-DCL, the
DL network is trained to predict highly correlated signals with a unique loss
function from a set of PW data, and the trained DL model encourages
high-quality PWI from low-quality single PW data. In addition, the DL-DCL
framework based on complex baseband signals enables a universal beamformer. To
assess the performance of DL-DCL, simulation, phantom and in vivo studies were
conducted with public datasets, and it was compared with traditional
beamformers (i.e., DAS with 75-PWs and DMAS with 1-PW) and other DL-based
methods (i.e., supervised learning approach with 1-PW and generative
adversarial network (GAN) with 1-PW). From the experiments, the proposed DL-DCL
showed comparable results with DMAS with 1-PW and DAS with 75-PWs in spatial
resolution, and it outperformed all comparison methods in contrast resolution.
These results demonstrated that the proposed unsupervised learning approach can
address the inherent limitations of traditional PWIs based on DL, and it also
showed great potential in clinical settings with minimal artifacts
METHOD FOR THE PRODUCTION OF HIGH SATURATED, LOW POLYUNSATURATED SOYBEAN OIL
Methods of genetically modifying soybean plants to alter the fatty acid properties of the oil are described
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