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Asymptotic Optimality of Myopic Optimization in Trial-Offer Markets with Social Influence

Andrés Abeliuk, Gerardo Berbeglia, Felipe Maldonado, Pascal Van Hentenryck
IJCAI 2016 pp. 2458-2464
/ijcai/2016/abeliuk2016ijcai-asymptotic/

Abstract

We study dynamic trial-offer markets, in which participants first try a product and later decide whether to purchase it or not. In these markets, social influence and position biases have a greater effect on the decisions taken in the sampling stage than those in the buying stage. We consider a myopic policy that maximizes the market efficiency for each incoming participant, taking into account the inherent quality of products, position biases, and social influence. We prove that this myopic policy is optimal and predictable asymptotically. PDF

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Text

Abeliuk et al. "Asymptotic Optimality of Myopic Optimization in Trial-Offer Markets with Social Influence." International Joint Conference on Artificial Intelligence, 2016.

Markdown

[Abeliuk et al. "Asymptotic Optimality of Myopic Optimization in Trial-Offer Markets with Social Influence." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/abeliuk2016ijcai-asymptotic/)

BibTeX

@inproceedings{abeliuk2016ijcai-asymptotic,
  title     = {{Asymptotic Optimality of Myopic Optimization in Trial-Offer Markets with Social Influence}},
  author    = {Abeliuk, Andrés and Berbeglia, Gerardo and Maldonado, Felipe and Van Hentenryck, Pascal},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {2458-2464},
  url       = {https://mlanthology.org/ijcai/2016/abeliuk2016ijcai-asymptotic/}
}