W
e apply random graph modeling methodology to analyze bipartite consumer-product graphs that repre-
sent 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 clus-
ter. 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 recom-
mendation 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 recommenda-
tion 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 outper-
forms 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.