Title :
Recommender Systems Based on an Active Data Warehouse with Text Documents
Author :
Felden, Carsten ; Chamoni, Peter
Author_Institution :
Fakultat fur Wirtschaftswissenschaft, Technische Univ. Bergakademie Freiberg
Abstract :
In everyday life one has to take a variety of decisions. So there is a need for recommendation which information may be relevant and which is rather unimportant to support decision making. Frequently we find recommendation systems to assist clients in online-shops and other Internet environments. The objective of these systems is the implementation of user-friendly interfaces with a high degree of personalization and efficient decision support. This paper evaluates several machine-learning techniques for recommendation systems which are suitable to find appropriate decision-relevant text documents like product descriptions or test reports. We propose recommendation systems which are based on an active data warehouse where we link adaptive user profiles and textual product descriptions
Keywords :
data warehouses; decision making; human factors; information filters; learning (artificial intelligence); retail data processing; text analysis; user interfaces; Internet environments; active data warehouse; adaptive user profiles; decision-relevant text documents; machine-learning techniques; online-shops; recommender systems; textual product descriptions; user-friendly interfaces; Books; Conference management; Data warehouses; Decision making; Information analysis; Internet; Manufacturing; Recommender systems; System testing; Technology management;
Conference_Titel :
System Sciences, 2007. HICSS 2007. 40th Annual Hawaii International Conference on
Conference_Location :
Waikoloa, HI
Electronic_ISBN :
1530-1605
DOI :
10.1109/HICSS.2007.460