Evaluation of Collaborative Filtering by Agent-Based Simulation Considering Market Environment

Abstract

We propose a new evaluation approach for collaborative filtering, a kind of recommendation algorithm through agent-based simulation. We modeled a virtual E-commerce market where we evaluated the collaborative filtering algorithm. Our findings were as follows: 1) the number of neighbors is a key parameter and there is a trade-off due to market circumstances, 2) a bigger number of neighbors performed better, with a tendency that was independent of the degree of clustering of consumer preferences, 3) if there were any high-frequency purchasers, a smaller number of neighbors performed better.