TY - GEN
T1 - Prediction in economic networks
T2 - International Conference on Information Systems, ICIS 2012
AU - Dhar, Vasant
AU - Geva, Tomer
AU - Oestreicher-Singer, Gal
AU - Sundararajan, Arun
PY - 2012
Y1 - 2012
N2 - We define an economic network as a linked set of products, where links are created by realizations of shared outcomes between entities. We analyze the predictive information contained in an increasingly prevalent type of economic network, a "product network" that links the landing pages of goods frequently co-purchased on e-commerce websites. Our data include one million books in 400 categories spanning two years, with over 70 million observations. Using autoregressive and neural-network models, we demonstrate that combining historical demand of a product with that of its neighbors improves demand predictions even as the network changes over time. Furthermore, network properties such as clustering and centrality contribute significantly to predictive accuracy. To our knowledge, this is the first large-scale study showing that a non-static product network contains useful distributed information for demand prediction, and that this information is more effectively exploited by integrating composite structural network properties into one's predictive models.
AB - We define an economic network as a linked set of products, where links are created by realizations of shared outcomes between entities. We analyze the predictive information contained in an increasingly prevalent type of economic network, a "product network" that links the landing pages of goods frequently co-purchased on e-commerce websites. Our data include one million books in 400 categories spanning two years, with over 70 million observations. Using autoregressive and neural-network models, we demonstrate that combining historical demand of a product with that of its neighbors improves demand predictions even as the network changes over time. Furthermore, network properties such as clustering and centrality contribute significantly to predictive accuracy. To our knowledge, this is the first large-scale study showing that a non-static product network contains useful distributed information for demand prediction, and that this information is more effectively exploited by integrating composite structural network properties into one's predictive models.
KW - Autoregressive models
KW - Co-purchase network
KW - Network-based prediction
KW - Neural networks
KW - PageRank
KW - Prediction
KW - Predictive modeling
KW - Product networks
UR - http://www.scopus.com/inward/record.url?scp=84886452599&partnerID=8YFLogxK
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AN - SCOPUS:84886452599
SN - 9781627486040
T3 - International Conference on Information Systems, ICIS 2012
SP - 1119
EP - 1136
BT - International Conference on Information Systems, ICIS 2012
Y2 - 16 December 2012 through 19 December 2012
ER -