A Smart B2C e-Commerce System Based

on ACP ApproachChen Lyu, Lefei Li, and Tao Pan, Tsinghua University

Intelligent Systems, IEEE, vol. 29, issue. 4, pp. 102 – 104, July-Aug. 2014

Framework of B2C service

Recommendation System

• Based on Artificial systems, computational experiment, and parallel execution (ACP) approach.

• Core of which is to use agent-based modeling and the simulation method.

• For ACP-based Recommendation System, ARS

The Agent-Based Modeling of ARS

Architecture of ARS

• Real system• When users register…• Information－ include users’ individual preference.• Actions－ include surfing webpages, browsing, and

making purchases.

• Artificial system• Set environment and initialize the simulation with the

certain frequency.• By running the simulation, we get the items that

“similar” agents (called neighbor agents) tend to buy, and recommend these to specific user.

The Recommendation Process of ARS

Create neighbor

agents

Calculate purchase

motivation

Count purchase results

Insert to recommend-ation results

The Consumer Decision Model

• Purchase Motivation：

Computational Experiments

• Develop the agent-based consumer decision model and perform computational experiments on the software platform of AnyLogic.

Results