RESEARCH ARTICLE RECOMMENDATION NETWORKS AND THE LONG TAIL OF ELECTRONIC COMMERCE1

It has been conjectured that the peer-based recommendations associated with electronic commerce lead to a redistribution of demand from popular products or “blockbusters ” to less popular or “niche ” products, and that electronic markets will therefore be characterized by a “long tail ” of demand and revenue. We test this conjecture using the revenue distributions of books in over 200 distinct categories on Amazon.com and detailed daily snapshots of co-purchase recommendation networks in which the products of these categories are situated. We measure how much a product is influenced by its position in this hyperlinked network of recommendations using a variant of Google’s PageRank measure of centrality. We then associate the average influence of the network on each category with the inequality in the distribution of its demand and revenue, quantifying this inequality using the Gini coefficient derived from the category’s Lorenz curve. We establish that categories whose products are influenced more by the recommendation network have significantly flatte