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Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems

Department of Supply Chain and Information Systems, Pennsylvania State University, 419 Business Building, University Park, Pennsylvania 16802
Department of Management Information Systems, The University of Arizona, McClelland Hall 430, 1130 East Helen Street, Tucson, Arizona 85721
Department of Management Information Systems, The University of Arizona, McClelland Hall 430, 1130 East Helen Street, Tucson, Arizona 85721
zanhuang{at}psu.edu
zeng{at}eller.arizona.edu
hchen{at}eller.arizona.edu

We apply random graph modeling methodology to analyze bipartite consumer-product graphs that represent sales transactions to better understand consumer purchase behavior in e-commerce settings. Based on two real-world e-commerce data sets, we found that such graphs demonstrate topological features that deviate significantly from theoretical predictions based on standard random graph models. In particular, we observed consistently larger-than-expected average path lengths and a greater-than-expected tendency to cluster. Such deviations suggest that the consumers’ product choices are not random even with the consumer and product attributes hidden. Our findings provide justification for a large family of collaborative filtering-based recommendation algorithms that make product recommendations based only on previous sales transactions. By analyzing the simulated consumer-product graphs generated by models that embed two representative recommendation algorithms, we found that these recommendation algorithm-induced graphs generally provided a better match with the real-world consumer-product graphs than purely random graphs. However, consistent deviations in topological features remained. These findings motivated the development of a new recommendation algorithm based on graph partitioning, which aims to achieve high clustering coefficients similar to those observed in the real-world e-commerce data sets. We show empirically that this algorithm significantly outperforms representative collaborative filtering algorithms in situations where the observed clustering coefficients of the consumer-product graphs are sufficiently larger than can be accounted for by these standard algorithms.

Key Words: random graph theory; consumer-purchase behavior; topological features; recommender systems; collaborative filtering
History: Received: September 8, 2004;