Algorithms for clustering data
Algorithms for clustering data
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
Machine learning and data mining
Communications of the ACM
Marketing on the internet - who can benefit from an online marketing approach
Decision Support Systems
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Database System Concepts
Database Management Systems
Data Warehousing Fundamentals
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Intelligent E-marketing with Web Mining, Personalization, and User-Adpated Interfaces
Industrial Conference on Data Mining: Advances in Data Mining, Applications in E-Commerce, Medicine, and Knowledge Management
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Computers and Operations Research
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
AdROSA-Adaptive personalization of web advertising
Information Sciences: an International Journal
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Expert Systems with Applications: An International Journal
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
A personality-based simulation of bargaining in e-commerce
Simulation and Gaming
On discovery of soft associations with "most" fuzzy quantifier for item promotion applications
Information Sciences: an International Journal
Computers and Operations Research
A heuristic personality-based bilateral multi-issue bargaining model in electronic commerce
International Journal of Human-Computer Studies
Key factors in forming an e-marketplace: An empirical analysis
Electronic Commerce Research and Applications
Direct and indirect effects of retail promotions on sales and profits in the do-it-yourself market
Expert Systems with Applications: An International Journal
Classification-based collaborative filtering using market basket data
Expert Systems with Applications: An International Journal
Why promotion strategies based on market basket analysis do not work
Expert Systems with Applications: An International Journal
A personalized recommendation system based on product taxonomy for one-to-one marketing online
Expert Systems with Applications: An International Journal
Feature-based recommendations for one-to-one marketing
Expert Systems with Applications: An International Journal
Customer-adapted coupon targeting using feature selection
Expert Systems with Applications: An International Journal
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
A probabilistic reputation model based on transaction ratings
Information Sciences: an International Journal
A rule-based method for identifying the factor structure in customer satisfaction
Information Sciences: an International Journal
Journal of Intelligent Manufacturing
| Hi-index | 0.07 |
In recent years, several techniques have been proposed to model electronic promotions for existing customers. However, these techniques are not applicable for new customers with no previous profile or behavior data. This study models promotions to new customers in an electronic marketplace. We introduce a multi-valued k-Nearest Neighbor (mkNN) learning capability for modeling promotions to new customers. In this modified learning algorithm, instead of a single product category, the seller sends the new customer a promotion on a variable set of m categories (where m is a variable) with the highest rank of desirability among the most similar previous customers. Previous studies consider sellers' profits in promotion and marketing models. In addition to the sellers' profits, three important factors -annoyance of customers, sellers' reputations, and customers' anonymity - are considered in this study. Without considering the customer's profile, we minimize unrelated and disliked offers to reduce the customer's annoyance and elevate the seller's reputation. The promotion models are evaluated in two separate experiments on populations with different degrees of optimism: (1) with fixed number of customers; and (2) in a fixed period of time. The evaluation is based on the parameters of customer population size and behavior as well as time interval, seller payoff, seller reputation, and the number of promotions canceled by the customers. The simulation results demonstrate that the proposed mkNN-based promotion strategies are moderately efficient with respect to all parameters for providing services in a large population. In addition, purchasing preferences of past customers, which are based on periodic promotions that a seller sends to customers, can generate future rapidly expanding demands in the market. By using these approaches, an advertising company can send acceptable promotions to customers without having specific profile information.