2019/06/26 by Pavlos Mitsoulis-Ntompos, Meisam Hejazinia, Mitsoulis-Ntompos, Pavlos +5 · 1 citation
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.1906.11336
arXiv admin note: substantial text overlap with arXiv:1907.02822
arxiv created 2019/08/06 · arxiv updated 2019/08/08
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.