Exploiting User Preferences for Multiscenarios in Query-Less Search

Abstract

Online travel platforms (OTPs), for example, booking.com, Ctrip.com , and Fliggy, deliver travel experiences to online users by providing travel-related products. Hotel recommendation is significantly important for OTPs since hotel bookings account for almost half of the travel expenses and hotel products generate more than half of OTP's revenues. More than 58% Fliggy users may choose to use query-less hotel searches to find candidate hotels, where no additional keywords are given except the expected check-in date and travel destination city. Thus, how to recommend hotels to traveler users is important and challenging. In this article, we explore the unique characteristics of query-less hotel users and propose a novel multiscenario queryless search network (MSQS). According to their searching date, expected check-in date, current city, and expected hotel city, MSQS groups users' behaviors (e.g., click, purchase, search) into four scenario groups, namely today-local, today-nonlocal, future-local, and future-nonlocal. The key components of MSQS are the global expert, the scenario expert, and the feedback expert. The global expert learns common features among different scenarios and extracts the feature interactions between context, users, and hotels. The scenario expert utilizes multilayer perception to learn the differentiating features between scenarios. The feedback expert learns users' preferences for hotels in different scenarios through their historical behaviors, and a scenario interest extractor is carefully designed to enhance attention across scenarios and behaviors. An offline experiment on the Fliggy production dataset with over 8 million users and 0.49 million travel items and an online AB test both show that MSQS effectively predicts users' hotel booking intentions.