The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
An Associative Classification-Based Recommendation System for Personalization in B2C E-Commerce Applications
Abstract
This chapter presents an associative classification-based recommendation system to support online customer decision-making when facing a huge amount of choices. Recommendation systems have been recently introduced to e-commerce sites in order to solve the information overload and mass confusion problem. This chapter applies knowledge discovery techniques to overcome the drawback of conventional approaches to recommendation systems. The framework of the associative classification-based recommendation system has been addressed in this chapter. The system analysis, design, and implementation issues in an Internet programming environment are also presented. Taking the advantage of accumulative knowledge from historical data, the efficiency and effectiveness of B2C e-commerce applications are improved.
Related Content
Vincent Lennard Kraus.
© 2023.
32 pages.
|
Tlou Maggie Masenya.
© 2023.
16 pages.
|
Arzu Tufan, Gurkan Tuna.
© 2023.
30 pages.
|
Wasswa Shafik.
© 2023.
19 pages.
|
Calvin Nobles, Sharon L. Burton, Darrell Norman Burrell.
© 2023.
23 pages.
|
Darrell Norman Burrell, Calvin Nobles, Austin Cusak, Laura Ann Jones, Jorja B. Wright, Horace C. Mingo, Jennifer Ferreras-Perez, Katrina Khanta, Philip Shen, Kevin Richardson.
© 2023.
16 pages.
|
Jorja B. Wright, Darrell Norman Burrell.
© 2023.
12 pages.
|
|
|