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.
More generally, in the second part we look at a model where the recommendee loses trust in the recommender, i.e. for each unsuccessful recommendation she becomes less and less inclined to listen to any further suggestion. This is most likely to appear when the recommendee has the feeling that the recommendations are dishonest''. How honest recommendations can be ensured when there are several recommenders is studied in @cite_22 . The approach suggested by the authors involves evaluating ranking recommenders based on the rating given to their recommended items by other people. This motivates recommenders to give good recommendations in a similar way that Ebay's rating system gives incentives for both buyers and sellers to behave''. This approach, however, requires a public market where potential buyers can look for recommendations. This is not the setting of personal recommendations considered here.
@cite_22
1972292875
This paper presents HYRIWYG (How You Rate Influences What You Get), a reputation system applicable to Internet Recommendation Systems (RS). The novelty lies in the incentive mechanism that encourages evaluators to volunteer their true opinion. Honesty is encouraged because rewards are indexed by the quality of the RS's suggestions.