Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates and optimal ad placement, there has been little consideration given to developing a coherent system for end-to-end customer journey optimization. Such a system would encompass all aspects of the customer experience, from presenting the right product value to the right users, to understanding a user's likelihood of conversion and long-term value to the platform, as well as their propensity for cross-selling and risk of churning. Currently, models and algorithms for customer journey optimization are often developed in isolation, leading to inefficiencies in modeling and data pipelines. Furthermore, the customer is often viewed as a collection of different entities by different organizational departments (such as marketing, sales, and finance), which can lead to additional friction in the customer experience. This workshop seeks to bridge the gap between academic researchers and industrial practitioners who are interested in building holistic solutions for end-to-end customer journey optimization. In addition, with the rising popularity of generative AI and LLM, we want to use this venue to exchange ideas regarding their applications in different stages of customer journey, and how the new technologies could help businesses achieve their objectives.

One of the long-standing questions in search systems is the role of diversity in results. From a product perspective, showing diverse results provides the user with more choice and should lead to an improved experience. However, this intuition is at odds with common machine learning approaches to ranking which directly optimize the relevance of each individual item without a holistic view of the result set. In this paper, we describe our journey in tackling the problem of diversity for Airbnb search, starting from heuristic based approaches and concluding with a novel deep learning solution that produces an embedding of the entire query context by leveraging Recurrent Neural Networks (RNNs). We hope our lessons learned will prove useful to others and motivate further research in this area.

The application of deep learning to search ranking was one of the most impactful product improvements at Airbnb. But what comes next after you launch a deep learning model? In this paper we describe the journey beyond, discussing what we refer to as the ABCs of improving search: A for architecture, ℬ for bias and ℂ for cold start. For architecture, we describe a new ranking neural network, focusing on the process that evolved our existing DNN beyond a fully connected two layer network. On handling positional bias in ranking, we describe a novel approach that led to one of the most significant improvements in tackling inventory that the DNN historically found challenging. To solve cold start, we describe our perspective on the problem and changes we made to improve the treatment of new listings on the platform. We hope ranking teams transitioning to deep learning will find this a practical case study of how to iterate on DNNs.

The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!

Airbnb is a two-sided rental marketplace offering a variety of unique and more traditional accommodation options. Similar to other online marketplaces we invest in optimizing the content surfaced on the search UI and ranking relevance to improve the guest online search experience. The unique Airbnb inventory, however, surfaces some major data challenges. Given the high stakes of booking less traditional accommodations, users can spend many days to weeks searching and scanning the description page of many accommodation ”listings” before making a decision to book. Moreover, much of the information about a listing is unstructured and can only be found by the user after they go through the details on the listing page. As a result, we have found traditional search metrics do not work well in the context of our platform. Basic metrics of single user actions, such as click-through-rates, number of listings viewed, or dwell time, are not consistently directionally correlated with our downstream business metrics. To address these issues we leverage machine learning to isolate signals of intent from rich behavioral data. These signals have key applications including analytical insights, ranking modeling inputs, and experimentation velocity. In this paper, we describe the development of a model-based user intent metric, ”intentful listing view”, which combines the signals of a variety of user micro-actions on the listing description page. We demonstrate this learned metric is directionally correlated with downstream conversion metrics and sensitive across a variety of historical search experiments.