Recommendation Networks and the Long Tail of Electronic Commerce1
作者研究了电商推荐网络是否会导致需求从热门产品（爆款）向冷门产品（长尾）转移。他们利用亚马逊图书200多个品类的收入分布数据，以及每日更新的“一起购买”推荐网络快照，用类似谷歌PageRank的方法测量每个产品在推荐网络中的影响力。然后，将品类中产品的平均网络影响力与该品类需求和收入分布的不平等程度（用基尼系数衡量）关联起来。结果发现，受推荐网络影响越大的品类，其需求和收入分布越平坦。具体来说，品类平均网络影响力翻倍时，最不畅销的20%产品的相对收入平均增加约50%，而最畅销的20%产品的相对收入平均减少约15%。这种效应在推荐网络同配性更高、聚类系数更低时更强，且在品类中产品受推荐影响更均匀时更明显。结论随时间、需求和收入分布、以及日和周聚合数据均稳健。
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 flatter demand and revenue distributions, even after controlling for variation in average category demand, category size, and price differentials. Our empirical findings indicate that doubling the average network influence on a category is associated with an average increase of about 50 percent in the relative revenue for the least popular 20 percent of products, and with an average reduction of about 15 percent in the relative revenue for the most popular 20 percent of products. We also show that this effect is enhanced by higher assortative mixing and lower clustering in the network, and is greater in categories whose products are more evenly influenced by recommendations. The direction of these results persists over time, across both demand and revenue distributions, and across both daily and weekly demand aggregations. Our work illustrates how the microscopic economic data revealed by online networks can be used to define and answer new kinds of research questions, offers a fresh perspective on the influence of networked IT artifacts on business outcomes, and provides novel empirical evidence about the impact of visible recommendations on the long tail of electronic commerce.