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arXiv · 1906.11336

A Simple Deep Personalized Recommendation System

Pavlos Mitsoulis-Ntompos·Meisam Hejazinia·Serena Zhang·Travis Brady
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Abstract

Recommender systems are critical tools to match listings and travelers in two-sided vacation rental marketplaces. Such systems require high capacity to extract user preferences for items from implicit signals at scale. To learn those preferences, we propose a Simple Deep Personalized Recommendation System to compute travelers' conditional embeddings. Our method combines listing embeddings in a supervised structure to build short-term historical context to personalize recommendations for travelers. Deployed in the production environment, this approach is computationally efficient and scalable, and allows us to capture non-linear dependencies. Our offline evaluation indicates that traveler embeddings created using a Deep Average Network can improve the precision of a downstream conversion prediction model by seven percent, outperforming more complex benchmark methods for online shopping experience personalization.

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Pavlos Mitsoulis-Ntompos, Meisam Hejazinia, Serena Zhang, Travis Brady. 2019-06-26. A Simple Deep Personalized Recommendation System. https://arxiv.org/abs/1906.11336

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