Recommendation Systems and Self Motivated Users

TL;DR

This paper addresses the challenge of designing recommendation systems that align individual user incentives with the system's exploration goals, proposing an incentive-compatible mechanism for sequential user settings.

Contribution

It introduces a novel incentive-compatible and asymptotically optimal mechanism for recommendation systems with self-motivated users in sequential settings.

Findings

Proposes a mechanism that aligns user incentives with system exploration.

Ensures asymptotic optimality in the proposed recommendation framework.

Highlights the complexity of designing such systems for general scenarios.

Abstract

Modern recommendation systems rely on the wisdom of the crowd to learn the optimal course of action. This induces an inherent mis-alignment of incentives between the system's objective to learn (explore) and the individual users' objective to take the contemporaneous optimal action (exploit). The design of such systems must account for this and also for additional information available to the users. A prominent, yet simple, example is when agents arrive sequentially and each agent observes the action and reward of his predecessor. We provide an incentive compatible and asymptotically optimal mechanism for that setting. The complexity of the mechanism suggests that the design of such systems for general settings is a challenging task.