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

Zan Huang (), Daniel D. Zeng () and Hsinchun Chen ()
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Zan Huang: Department of Supply Chain and Information Systems, Pennsylvania State University, 419 Business Building, University Park, Pennsylvania 16802
Daniel D. Zeng: Department of Management Information Systems, The University of Arizona, McClelland Hall 430, 1130 East Helen Street, Tucson, Arizona 85721
Hsinchun Chen: Department of Management Information Systems, The University of Arizona, McClelland Hall 430, 1130 East Helen Street, Tucson, Arizona 85721

Management Science, 2007, vol. 53, issue 7, 1146-1164

Abstract: 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.

Keywords: random graph theory; consumer-purchase behavior; topological features; recommender systems; collaborative filtering (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (22)

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