Welfare-Optimized Recommender Systems

TL;DR

This paper introduces a welfare-optimized recommender system that models shoppers as rational decision makers, integrating price and willingness-to-pay to enhance recommendation strategies and unify retailer and shopper objectives.

Contribution

It proposes a novel welfare-based recommendation framework using the Random Utility Model, unifying commercial and consumer goals in a single metric.

Findings

Demonstrates effectiveness on synthetic data across various scenarios

Unifies retailer and shopper objectives into a single welfare metric

Opens new avenues for couponing and price optimization

Abstract

We present a recommender system based on the Random Utility Model. Online shoppers are modeled as rational decision makers with limited information, and the recommendation task is formulated as the problem of optimally enriching the shopper's awareness set. Notably, the price information and the shopper's Willingness-To-Pay play crucial roles. Furthermore, to better account for the commercial nature of the recommendation, we unify the retailer and shoppers' contradictory objectives into a single welfare metric, which we propose as a new recommendation goal. We test our framework on synthetic data and show its performance in a wide range of scenarios. This new framework, that was absent from the Recommender System literature, opens the door to Welfare-Optimized Recommender Systems, couponing, and price optimization.