Booking.com @ RecSys 2021
Booking.com’s publications at the 15th ACM conference on Recommender Systems
Booking.com’s mission is to make it easier for everyone to experience the world. By investing in the technology that helps take the friction out of travel, we seamlessly connects millions of travelers with memorable experiences, transportation options and incredible places to stay. One of the way to do so, is through an unprecedented personalized experience for each of our customers. We’re happy to announce that Booking.com is a platinum sponsor of ACM RecSys 2021 in Amsterdam. Check out the work we do on Recommender Systems and will be presented at the conference.
Recommender Systems for Personalized User Experience: Lessons learned at Booking.com
Industry Talk / Posters Session
Ioannis Kangas, Maud Schwoerer, Lucas Bernardi
Booking.com is the world’s leading online travel platform where users make many decisions supported by our recommendations, such as destinations, travel dates, facilities, etc. This leads to a complex User Interface (UI) containing many widgets of different relevance for different users. We address the problem of constructing an optimal UI, a non-trivial problem, mainly due to user preferences evolving over time and multiple independent teams collaboratively building the UI. Our goal is to provide a personalized User Experience (UX) which adapts to changes in the environment and ensures governable, collaborative product development. The solution relies on a Multi Armed Bandits (MAB) framework currently allowing product teams to collaborate on the construction of UIs and serving millions of users every day. We present examples of our solution and lessons learned during their implementation.
I Know What You Did Next Summer: Challenges in Travel Destination Recommendation
Dima Goldenberg, Sarai Mizrachi, Adam Horowitz, Ioannis Kangas, Or Levkovich, Alessandro Mozzato, Maud Schwoerer, Michele Ferretti, Panagiotis Korvesis, Lucas Bernardi
Picking a destination is the first step of any trip planning. While some travelers have an exact destination in mind, others have some degree of open-mindedness and can thus benefit from receiving destination recommendations. Online travel platforms often actively recommend a wide variety of travel destinations to their customers; however, customers at different stages of trip planning may expect different recommendations to serve different needs such as good alternative destinations or destinations for extending a trip. This makes recommending a destination challenging, requiring a complex synthesis of available contextual data along with consideration of a customer’s needs and the holistic user experience of receiving recommendations. At a technical level, destinations also represent complex entities with ambiguous geographical properties and naming conventions. In this paper, we discuss the technical and customer-centric challenges of building real-life destination recommendation systems. We supplement this by providing applied solution examples from Booking.com, one of the world’s leading online travel platforms.
Optimization Levers for Promotions Personalization Under Limited Budget
Dima Goldenberg, Javier Albert, Guy Tsype
Modern e-commerce platforms make use of promotional offers, such as discounts and rewards, to encourage customers to complete purchases. As expected, revenue is also affected by promotions, and a dedicated budget usually limits monetary losses. In order to allocate promotions efficiently within budget constraints, a marketer can use causal machine learning based personalization along with constrained optimization tools.
In this paper we study four decision levers of promotional campaigns, allowing optimal and personalized offers allocation within budget constraints. We demonstrate the optimization problems in real-life promotional campaigns and formulate them as variations of the Knapsack problem, allowing us to introduce efficient applied solutions. We demonstrate that such solutions have a significant impact on promotional campaigns at Booking.com — a world leading online travel platform.
Simulations in Recommender Systems : An Industry Perspective
SimuRec’ 21 Workshop
Lucas Bernardi, Sakshi Batra, Cintia Bruscantini
The construction of effective Recommender Systems (RS) is a complex process, mainly due to the nature of RSs which involves large scale software-systems and human interactions. Iterative development processes require deep understanding of a current baseline as well as the ability to estimate the impact of changes in multiple variables of interest. Simulations are well suited to address both challenges and potentially leading to a high velocity construction process, a fundamental requirement in commercial contexts. Recently, there has been significant interest in RS Simulation Platforms, which allow RS developers to easily craft simulated environments
where their systems can be analysed. In this work we discuss how simulations help to increase velocity, we look at the literature around RS Simulation Platforms, analyse strengths and gaps and distill a set of guiding principles for the design of RS Simulation Platforms that we believe will maximize the velocity of iterative RS construction processes.
Previous Booking.com work on Recommender Systems
Recommenders systems are key components of Booking.com ML portfolio. Check out our previous publications on the subject:
- Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints
Dima Goldenberg, Javier Albert, Lucas Bernardi, Pablo Estevez- RecSys ‘20 - 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com
Lucas Bernardi, Themis Mavridis, Pablo Estevez- KDD ‘19 - Booking.com Challenge
Dima Goldenberg, Kostia Kofman, Pavel Levin, Sarai Mizrachi, Maayan Kafry, Guy Nadav- WebTour @ WSDM ‘21 - Combining Context Features in Sequence-Aware Recommender Systems Sarai Mizrachi, Pavel Levin — RecSys ‘19
- Beyond algorithms: Ranking at scale at Booking.com
Themis Mavridis, Soraya Hausl, Andrew Mende, Roberto Pagano- ComplexRec @ RecSys ‘20 - Recommending Accommodation Filters with Online Learning
Lucas Bernardi, Pablo Estevez, Matias Eidis, EQbal Osama- ORSUM @ RecSys ‘20 - Personalization in Practice: Methods and Applications
Dima Goldenberg, Kostia Kofman, Javier Albert, Sarai Mizrachi, Adam Horowitz, Irene Teinemaa- WSDM ‘21 - Booking.com Multi-Destination Trips Dataset
Dima Goldenberg, Pavel Levin, — SIGIR ‘21 - Uplift Modeling: From Causal Inference to Personalization
Irene Teinemaa, Javier Albert, Dima Goldenberg- WWW ‘21
