Leveraging laziness, browsing-pattern aware stacked models for sequential accommodation learning to rank

In this paper we provide an overview of the approach we used as team PoliCloud8 for the ACM RecSys Challenge 2019. The competition, organized by Trivago, focuses on the problem of session-based and context-aware accommodation recommendation in a travel domain. The goal is to suggest suitable accommodations fitting the needs of the traveller to maximise the chance of a redirect (clickout) to a booking site, relying on explicit and implicit user signals within a session (clicks, search refinement, filter usage) to detect the users intent. Our team proposes a solution based on several new features, designed to capture specific types of information as well as some well-known models: gradient boosting, neural networks and a stacking-based ensemble.