TY - GEN
T1 - Aligning popularity and quality in online cultural markets
AU - Van Hentenryck, Pascal
AU - Abeliuk, Andrés
AU - Berbeglia, Franco
AU - Maldonado, Felipe
AU - Berbeglia, Gerardo
N1 - Publisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016
Y1 - 2016
N2 - Social influence is ubiquitous in cultural markets and plays an important role in recommendations for books, songs, and news articles to name only a few. Yet social influence is often presented in a bad light, often because it supposedly increases market unpredictability. Here we study a model of trial-offer markets, in which participants try products and later decide whether to purchase. We consider a simple policy which recovers product quality and ranks the products by quality when presenting them to market participants. We show that, in this setting, market efficiency always benefits from social influence. Moreover, we prove that the market converges almost surely to a monopoly for the product of highest quality, making the market both predictable and asymptotically optimal. Computational experiments confirm that the quality ranking policy quickly identifies "blockbusters", outperforms other policies, and is highly predictable.
AB - Social influence is ubiquitous in cultural markets and plays an important role in recommendations for books, songs, and news articles to name only a few. Yet social influence is often presented in a bad light, often because it supposedly increases market unpredictability. Here we study a model of trial-offer markets, in which participants try products and later decide whether to purchase. We consider a simple policy which recovers product quality and ranks the products by quality when presenting them to market participants. We show that, in this setting, market efficiency always benefits from social influence. Moreover, we prove that the market converges almost surely to a monopoly for the product of highest quality, making the market both predictable and asymptotically optimal. Computational experiments confirm that the quality ranking policy quickly identifies "blockbusters", outperforms other policies, and is highly predictable.
UR - http://www.scopus.com/inward/record.url?scp=84979642596&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016
SP - 398
EP - 407
BT - Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016
PB - AAAI Press
T2 - 10th International Conference on Web and Social Media, ICWSM 2016
Y2 - 17 May 2016 through 20 May 2016
ER -