Mechanism Design for Personalized Recommender Systems

Abstract

Strategic behaviour from sellers on e-commerce websites, such as faking transactions and manipulating the recommendation scores through artificial reviews, have been among the most notorious obstacles that prevent websites from maximizing the efficiency of their recommendations. Previous approaches have focused almost exclusively on machine learning-related techniques to detect and penalize such behaviour. In this paper, we tackle the problem from a different perspective, using the approach of the field of mechanism design. We put forward a game model tailored for the setting at hand and aim to construct truthful mechanisms, i.e. mechanisms that do not provide incentives for dishonest reputation-augmenting actions, that guarantee good recommendations in the worst-case. For the setting with two agents, we propose a truthful mechanism that is optimal in terms of social efficiency. For the general case of m agents, we prove both lower and upper bound results on the effciency of truthful mechanisms and propose truthful mechanisms that yield significantly better results, when compared to an existing mechanism from a leading e-commerce site on real data.

Feedback Elicitation Mechanism Design

“…Pr [1] = 0.3333. Given the service type θ , assume all agents except i have observed type 1 and they all tell the truth (i.e.…”

“…. , n 10 , the coefficients on the left side consist of submatrix D (1). Under proper condition, Pr[ n|2] Pr[ n|1] and Pr[ n|3] Pr[ n|1] reach minimum at the same time when n = n 1 .…”

“…Under proper condition, Pr[ n|2] Pr[ n|1] and Pr[ n|3] Pr[ n|1] reach minimum at the same time when n = n 1 . Obviously, if current solution satisfies the constraint [1], it must satisfy the other 9 constraints. Thus the first group of constraints clamped down to one constraint y…”

“…The associate editor coordinating the review of this manuscript and approving it for publication was Ziyan Wu. will do so [1], [2]. On the other hand, there is no way to verify whether a user's feedback is truthful or not.…”

Reinforcement Mechanism Design for e-commerce

“…Its main principle is the design of systems in such a way that the strategic behaviour of the participants will lead to outcomes that are aligned with the goals of the society, or the objectives of the designer. Cai et al [10] tackle the problem of faking transactions and fraudulent seller behaviour in e-commerce using the tools from the field of mechanism design. A common denominator in most of the classical work in economics is that the participants have access to either full information or some distributional estimate of the preferences of others.…”