Beyond Movie Recommendations: Solving the Continuous Cold Start Problem in E-commerceRecommendations
Julia Kiseleva, Alexander Tuzhilin, Jaap Kamps, Melanie J.I., Mueller, Lucas Bernardi, Chad Davis, Ivan Kovacek, Mats Stafseng, Einarsen, Djoerd Hiemstra

TL;DR
This paper addresses the Continuous Cold Start Problem in e-commerce by leveraging implicit contextual data to create user profiles, significantly improving engagement and click-through rates in live A/B testing.
Contribution
It introduces a method to build implicit contextual user profiles from transaction logs, enabling effective personalized ranking without user history.
Findings
Contextual profiles improve ranking accuracy.
Implicit cues from logs capture situational context.
20% increase in click-through rate in live A/B test.
Abstract
Many e-commerce websites use recommender systems or personalized rankers to personalize search results based on their previous interactions. However, a large fraction of users has no prior inter-actions, making it impossible to use collaborative filtering or rely on user history for personalization. Even the most active users mayvisit only a few times a year and may have volatile needs or different personas, making their personal history a sparse and noisy signal at best. This paper investigates how, when we cannot rely on the user history, the large scale availability of other user interactions still allows us to build meaningful profiles from the contextual data and whether such contextual profiles are useful to customize the ranking, exemplified by data from a major online travel agentBooking.com.Our main findings are threefold: First, we characterize the Continuous Cold Start…
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