Parametric solution for linear bicriteria knapsack models
Management Science
WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Mathematical Techniques for Efficient Record Segmentation in Large Shared Databases
Journal of the ACM (JACM)
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Algorithms for mining association rules in bag databases
Information Sciences—Informatics and Computer Science: An International Journal
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
A strategy-oriented operation module for recommender systems in e-commerce
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
A strategy-oriented operation module for recommender systems in E-commerce
Computers and Operations Research
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
A topic-based recommender system for electronic marketplace platforms
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Electronic commerce (EC) has become an important support for business. EC is regarded as an efficient platform to bridge the gape between the suppliers and consumers, but how to make use of a huge amount of transaction data and identify potential customers on the internet remains a challenge for an EC company. In particular, to recommend proper products to customers, the preferences of the targeted customers need to be accurately specified and their preferences should be taken into account. This is not only to show the goodwill of the company, but also to retain the customer relation. This study aims to construct a recommender system by focusing on the on-line decision support module with respect to customers' characteristics and supplier's profits. For effective decision support, a mathematical model is developed so that the right product can be recommended to the right person with the best profit for the company. A numerical example is used to illustrate how this model works when both supplier's and consumers' desires are taken into consideration to achieve an optimal Win-Win Strategy for market expansion.