Graphical E-Commerce Values Filtering Model in Spatial Database Framework

Abstract

Our customer preference model is based on aggregation of partly linear relaxations of value filters often used in e-commerce applications. Relaxation is motivated by the Analytic Hierarchy Processing method.

In low dimensions our method is well suited also for data visualization. The process of translating models to programs is formalized by Challenge-Response Framework CRF.

CRF resembles remote process call. In our case, the model is automatically translated to a program using spatial database features.

This enables us to define new metrics with spatial motivation. We provide experiments with simulated data (items) and users.

Keywords

• E-commerce values filtering
• spatial database
• recommender systems
• user preference learning
• pivot based indexing
• experiments
• spatial evaluation measures