A New Perspective on Recommender Systems A Random Graph Theory Approach
نویسندگان
چکیده
Random graph theory has become a major modeling tool to study complex systems. We apply random graph theory to analyze bipartite consumer-product graphs that represent sales transaction data to understand purchase behavior in e-commerce settings. Using two real-world e-commerce datasets we found that such graphs demonstrate topological features that deviate from theoretical predictions based on standard random graph models. In particular we observed consistently larger-than-expected average path lengths and clustering coefficients. Such deviations suggest that the consumer choices of products are not random and provide justification for a large family of collaborative filtering-based recommendation algorithms.
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