A Method of Acquiring Accurate Information from Fuzzy Demand
Pages 20 - 27
Abstract
With the popularization and application of big data technology and artificial intelligence technology, great breakthroughs have been made in the field of e-commerce. While, there are imperfections of user demand for major e-commerce platforms, for instance, incomplete expression of demand, difficult to deal with the voice input, difficult to deal with the fuzzy demand, unsustainable acquisition of demand. In this paper, a method of acquiring accurate information from fuzzy demand is proposed. This method is suitable for scenarios with fuzzy voice input, which makes up for the shortcomings of the existing e-commerce platform. It can help to reduce the expression burden on demand of transaction subject, also it meets his/her personalized needs with accurate recognition results. Furthermore, it enhances the experience of the transaction subject with flexible and controllable interaction methods.
References
[1]
Huang Y, Chai Y, Liu Y, Shen J, The architecture of next generation e-commerce platform, Tsinghua Science & Technology, vol. 22, no. 1, pp. 10--28, 2017
[2]
Huang Y, Chai Y, Liu Y, and Gu X, Holographic Personalized Portal for Industrial Ecological System, The International Conference on crowd science and engineering, pp. 9--13, 2017.
[3]
Wang Y, Xiong Q, An Overview of Development in E-Commerce Technology, Journal Of Wuhan University Of Science And Technology, vol.28, no.4, pp. 406--409, 2006.
[4]
Renee, G. Technology Choices Behind the E-commerce, InfoWorld, vol. 22, no. 3, pp. 66, 2000.
[5]
Tseng M M, Du X, Design by Customers for Mass Customization Products, CIRP Annals - Manufacturing Technology, vol. 47, no. 1, pp. 103--106, 1998.
[6]
Aggarwal C C, Yu P S. Data Mining Techniques for Personalization, Data Engineering Bulletin, vol. 23, no. 23, pp. 4--9, 2000.
[7]
Bruyn A D, Liechty J C, Huizingh E K R E, et al. Offering Online Recommendations with Minimum Customer Input through Conjoint-Based Decision Aids, Marketing Science, vol. 27, no. 3, pp. 443--460, 2008.
[8]
Hu X, Research on Recommender System Based on Product Attributes [D], Huazhong University of Science and Technology, 2012.
[9]
Smith KA, NgA, Web Page Clustering Using A Self-Organizing Map of User Navigation Patterns, Decision Support Systems, vol. 35, no. 2, pp. 245--256, 2003.
[10]
Moe W W, Buying, Searching, or Browsing: Differentiating between Online Shoppers Using In-Store Navigational Clickstream, Journal of Consumer Psychology, vol. 13, no.13, pp. 29--39, 2003.
[11]
Ying X, The Research On User Modeling for Internet Personalized Services [D], China National University of Defence Technology, 2003.
[12]
Claypool M, Le P, Wased M, et al, Implicit Interest Indicators, Proceedings of the 6th international conference on intelligent user interfaces, ACM, pp. 33--40, 2001.
Index Terms
- A Method of Acquiring Accurate Information from Fuzzy Demand
Recommendations
Fuzzy revenue for fuzzy demand quantity based on interval-valued fuzzy sets
Gorzalezany (Fuzzy Sets and Systems 21 (1987) 1) has mentioned the interval-valued fuzzy sets and its properties. In his paper, we may fuzzify the demand quantity d as D by considering the demand function P(d) = a - bd and revenue function R(d) = ad - ...
An inventory model for single-period products with reordering opportunities under fuzzy demand
This paper analyzes a single-period inventory model of profit maximization with a reordering strategy in an imprecise environment. The entire period is divided into two slots and the customer demand is considered as a fuzzy number in situations where ...
Supply chain coordination by revenue-sharing contract with fuzzy demand
In this paper, we study revenue-sharing contract in a supply chain with fuzzy demand. Under such a contract, a retailer pays a supplier a wholesale price for each unit purchased, plus a percentage of the revenue generated by the retailer that is ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2019
246 pages
ISBN:9781450376402
DOI:10.1145/3371238
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 October 2019
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- National Key Research & Development Plan of China
Conference
ICCSE'19
ICCSE'19: The 4th International Conference on Crowd Science and Engineering
October 18 - 21, 2019
Jinan, China
Acceptance Rates
ICCSE'19 Paper Acceptance Rate 35 of 92 submissions, 38%;
Overall Acceptance Rate 92 of 247 submissions, 37%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 45Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in