A Simple Deep Personalized Recommendation System
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.
Cite
@article{arxiv.1906.11336,
title = {A Simple Deep Personalized Recommendation System},
author = {Pavlos Mitsoulis-Ntompos and Meisam Hejazinia and Serena Zhang and Travis Brady},
journal= {arXiv preprint arXiv:1906.11336},
year = {2019}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1907.02822