No data? No problem! A Search-based Recommendation System with Cold Starts
Pedro M. Gardete, Carlos D. Santos

TL;DR
This paper introduces a search-based recommendation system that leverages consumer browsing behaviors and dynamic decision modeling to address cold start issues and improve profit outcomes.
Contribution
It presents a novel approach combining machine learning and Bellman equations to incorporate consumer search policies into recommendations, overcoming data limitations and accounting for state-dependent behaviors.
Findings
33% profit increase over seller recommendations
Browsing history and past recommendations have strong complementary effects
Effective churn management significantly enhances value creation
Abstract
Recommendation systems are essential ingredients in producing matches between products and buyers. Despite their ubiquity, they face two important challenges. First, they are data-intensive, a feature that precludes sophisticated recommendations by some types of sellers, including those selling durable goods. Second, they often focus on estimating fixed evaluations of products by consumers while ignoring state-dependent behaviors identified in the Marketing literature. We propose a recommendation system based on consumer browsing behaviors, which bypasses the "cold start" problem described above, and takes into account the fact that consumers act as "moving targets," behaving differently depending on the recommendations suggested to them along their search journey. First, we recover the consumers' search policy function via machine learning methods. Second, we include that policy into…
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