Learning to Comparison-Shop
Jie Tang, Daochen Zha, Xin Liu, Huiji Gao, Liwei He, Stephanie Moyerman, Sanjeev Katariya

TL;DR
This paper introduces the LTCS system, a novel ranking model that explicitly captures user comparison shopping behavior, leading to improved relevance, diversity, and user satisfaction in online marketplaces.
Contribution
The paper presents a new ranking architecture that models comparison shopping behavior, outperforming existing methods in relevance and user experience metrics.
Findings
NDCG improved by 1.7%
Booking conversion rate increased by 0.6%
Outperforms state-of-the-art models
Abstract
In online marketplaces like Airbnb, users frequently engage in comparison shopping before making purchase decisions. Despite the prevalence of this behavior, a significant disconnect persists between mainstream e-commerce search engines and users' comparison needs. Traditional ranking models often evaluate items in isolation, disregarding the context in which users compare multiple items on a search results page. While recent advances in deep learning have sought to improve ranking accuracy, diversity, and fairness by encoding listwise context, the challenge of aligning search rankings with user comparison shopping behavior remains inadequately addressed. In this paper, we propose a novel ranking architecture - Learning-to-Comparison-Shop (LTCS) System - that explicitly models and learns users' comparison shopping behaviors. Through extensive offline and online experiments, we…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Expert finding and Q&A systems · Consumer Market Behavior and Pricing
