On the Pricing of Recommendations and Recommending Strategically

TL;DR

This paper explores the economic and strategic aspects of recommendation pricing, analyzing truthful mechanisms, fairness, and trust dynamics to optimize long-term profit in recommendation systems.

Contribution

It introduces new fairness-based pricing mechanisms using game theory and analyzes trust decay effects on long-term recommendation profitability.

Findings

No truthful pricing mechanism exists with respect to the seller.

Fair pricing solutions like the Shapley value are proposed for multiple recommenders.

Trust decay limits long-term profit, linking to banner blindness phenomena.

Abstract

If you recommend a product to me and I buy it, how much should you be paid by the seller? And if your sole interest is to maximize the amount paid to you by the seller for a sequence of recommendations, how should you recommend optimally if I become more inclined to ignore you with each irrelevant recommendation you make? Finding an answer to these questions is a key challenge in all forms of marketing that rely on and explore social ties; ranging from personal recommendations to viral marketing. In the first part of this paper, we show that there can be no pricing mechanism that is "truthful" with respect to the seller, and we use solution concepts from coalitional game theory, namely the Core, the Shapley Value, and the Nash Bargaining Solution, to derive provably "fair" prices for settings with one or multiple recommenders. We then investigate pricing mechanisms for the setting where recommenders have different "purchase arguments". Here we show that it might be beneficial for the recommenders to withhold some of their arguments, unless anonymity-proof solution concepts, such as the anonymity-proof Shapley value, are used. In the second part of this paper, we analyze the setting where the recommendee loses trust in the recommender for each irrelevant recommendation. Here we prove that even if the recommendee regains her initial trust on each successful recommendation, the expected total profit the recommender can make over an infinite period is bounded. This can only be overcome when the recommendee also incrementally regains trust during periods without any recommendation. Here, we see an interesting connection to "banner blindness", suggesting that showing fewer ads can lead to a higher long-term profit.