DocumentCode
1862320
Title
Notice of Retraction
Combined Tactics Negotiation Model with Bayesian Learning in E-Commerce
Author
Liang Zhang ; Na Li
Author_Institution
Coll. of Economic, Tianjin Polytech. Univ., Tianjin, China
fYear
2010
fDate
9-10 Jan. 2010
Firstpage
403
Lastpage
406
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In the transaction process, the effective negotiation may cause more profits. In B2C and C2C, sellers only can use the fixed price or auction because they have no time and energy to bargain with buyers who are random on time and quantity, which reduces the benefits and successful transaction rate. This article established a negotiation model to solve this problem. This model conforms to the general negotiation flow and involves Bayesian learning function. In original Bayesian learning, the conditional probability is hard to obtain. The shortcoming is remedied in this model. It makes this negotiation model simple, effective and have learning ability.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In the transaction process, the effective negotiation may cause more profits. In B2C and C2C, sellers only can use the fixed price or auction because they have no time and energy to bargain with buyers who are random on time and quantity, which reduces the benefits and successful transaction rate. This article established a negotiation model to solve this problem. This model conforms to the general negotiation flow and involves Bayesian learning function. In original Bayesian learning, the conditional probability is hard to obtain. The shortcoming is remedied in this model. It makes this negotiation model simple, effective and have learning ability.
Keywords
Bayes methods; electronic commerce; learning (artificial intelligence); B2C; Bayesian learning; C2C; combined tactics negotiation model; e-commerce; transaction process; Automation; Bayesian methods; Computer industry; Data mining; Decision making; Educational institutions; Electronic commerce; Feedback; Power generation economics; Uncertainty; Bayesian learning; electronic commerce; negotiation model;
fLanguage
English
Publisher
ieee
Conference_Titel
Knowledge Discovery and Data Mining, 2010. WKDD '10. Third International Conference on
Conference_Location
Phuket
Print_ISBN
978-1-4244-5397-9
Type
conf
DOI
10.1109/WKDD.2010.18
Filename
5432569
Link To Document