2018 RealTimePersonalizationUsingEmb
- (Grbovic & Cheng, 2018) ⇒ Mihajlo Grbovic, and Haibin Cheng. (2018). “Real-time Personalization Using Embeddings for Search Ranking at Airbnb.” In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining.
Subject Headings: User Embedding, Item Embedding, Lambda Rank, AirBnB.
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Abstract
Search Ranking and Recommendations are fundamental problems of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fits-all solution does not exist in this space. Given a large difference in content that needs to be ranked, personalized and recommended, each marketplace has a somewhat unique challenge. Correspondingly, at Airbnb, a short-term rental marketplace, search and recommendation problems are quite unique, being a two-sided marketplace in which one needs to optimize for host and guest preferences, in a world where a user rarely consumes the same item twice and one listing can accept only one guest for a certain set of dates. In this paper we describe Listing and User Embedding techniques we developed and deployed for purposes of Real-time Personalization in Search Ranking and Similar Listing Recommendations, two channels that drive 99% of conversions. The embedding models were specifically tailored for Airbnb marketplace, and are able to capture guest's short-term and long-term interests, delivering effective home listing recommendations. We conducted rigorous offline testing of the embedding models, followed by successful online tests before fully deploying them into production.
1 INTRODUCTION
During last decade Search architectures, which were typically based on classic Information Retrieval, have seen an increased presence of Machine Learning in its various components [2], especially in Search Ranking which often has challenging objectives depending on the type of content that is being searched over. The main reason behind this trend is the rise in the amount of search data that can be collected and analyzed. The large amounts of collected data open up possibilities for using Machine Learning to personalize search results for a particular user based on previous searches and recommend similar content to recently consumed one.
The objective of any search algorithm can vary depending on the platform at hand. While some platforms aim at increasing website engagement (e.g. clicks and time spent on news articles that are being searched), others aim at maximizing conversions (e.g. purchases of goods or services that are being searched over), and in the case of two sided marketplaces we often need to optimize the search results for both sides of the marketplace, i.e. sellers and buyers. The two sided marketplaces have emerged as a viable business model in many real world applications. In particular, we have moved from the social network paradigm to a network with two distinct types of participants representing supply and demand. Example industries include accommodation (Airbnb), ride sharing (Uber, Lyft), online shops (Etsy), etc. Arguably, content discovery and search ranking for these types of marketplaces need to satisfy both supply and demand sides of the ecosystem in order to grow and prosper.
In the case of Airbnb, there is a clear need to optimize search results for both hosts and guests, meaning that given an input query with location and trip dates we need to rank high listings whose location, price, style, reviews, etc. are appealing to the guest and, at the same time, are a good match in terms of host preferences for trip duration and lead days. Furthermore, we need to detect listings that would likely reject the guest due to bad reviews, pets, length of stay, group size or any other factor, and rank these listings lower. To achieve this we resort to using Learning to Rank. Specifically, we formulate the problem as pairwise regression with positive utilities for bookings and negative utilities for rejections, which we optimize using a modified version of Lambda Rank [4] model that jointly optimizes ranking for both sides of the marketplace.
Since guests typically conduct multiple searches before booking, i.e. click on more than one listing and contact more than one host during their search session, we can use these in-session signals, i.e. clicks, host contacts, etc. for Real-time Personalization where the aim is to showto the guest more of the listings similar to the oneswe think they liked since staring the search session. At the same time we can use the negative signal, e.g. skips of high ranked listings, to show to the guest less of the listings similar to the ones we think they did not like. To be able to calculate similarities between listings that guest interacted with and candidate listings that need to be ranked we propose to use listing embeddings, low-dimensional vector representations learned from search sessions. We leverage these similarities to create personalization features for our Search Ranking Model and to power our Similar Listing Recommendations, the two platforms that drive 99% of bookings at Airbnb.
In addition to Real-time Personalization using immediate user actions, such as clicks, that can be used as proxy signal for shortterm user interest,we introduce another type of embeddings trained on bookings to be able to capture user’s long-term interest. Due to the nature of travel business, where users travel 1-2 times per year on average, bookings are a sparse signal, with a long tail of users with a single booking. To tackle this we propose to train embeddings at a level of user type, instead of a particular user id, where type is determined using many-to-one rule-based mapping that leverages known user attributes. At the same time we learn listing type embeddings in the same vector space as user type embeddings. This enables us to calculate similarities between user type embedding of the user who is conducting a search and listing type embeddings of candidate listings that need to be ranked. Compared to previously published work on embeddings for personalization on the Web, novel contributions of this paper are:
• Real-time Personalization - Most of the previous work on personalization and item recommendations using embeddings [8, 11] is deployed to production by forming tables of user-item and item-item recommendations offline, and then reading from them at the time of recommendation. We implemented a solution where embeddings of items that user most recently interacted with are combined in an online manner to calculate similarities to items that need to be ranked.
• Adapting Training for Congregated Search - Unlike in Web search, the search on travel platforms is often congregated, where users frequently search only within a certain market, e.g. Paris., and rarely across different markets. We adapted the embedding training algorithm to take this into account when doing negative sampling, which lead to capturing better within-market listings similarities.
• Leveraging Conversions as Global Context - We recognize the importance of click sessions that end up in conversion, in our case booking. When learning listing embeddings we treat the booked listing as global context that is always being predicted as the window moves over the session.
• User Type Embeddings - Previous work on training user embeddings to capture their long-term interest (Djuric et al., 2014, Weston et al., 2013) train a separate embedding for each user. When target signal is sparse, there is not enough data to train a good embedding representation for each user. Not to mention that storing embeddings for each user to perform online calculations would require lot of memory. For that reason we propose to train embeddings at a level of user type, where groups of users with same type will have the same embedding.
• Rejections as Explicit Negatives - To reduce recommendations that result in rejections we encode host preference signal in user and listing type embeddings by treating host rejections as explicit negatives during training.
For short-term interest personalization we trained listing embeddings using more than 800 million search clicks sessions, resulting in high quality listing representations. We used extensive offline and online evaluation on real search traffic which showed that adding embedding features to the ranking model resulted in significant booking gain. In addition to the search ranking algorithm, listing embeddings were successfully tested and launched for similar listing recommendations where they outperformed the existing algorithm click-through rate (CTR) by 20%.
For long-term interest personalization we trained user type and listing type embeddings using sequences of booked listings by 50 million users. Both user and listing type embeddings were learned in the same vector space, such that we can calculate similarities between user type and listing types of listings that need to be ranked. The similarity was used as an additional feature for search ranking model and was also successfully tested and launched.
2 RELATED WORK
In a number of Natural Language Processing (NLP) applications classic methods for language modeling that represent words as highdimensional, sparse vectors have been replaced by Neural Language models that learn word embeddings, i.e. low-dimensional representations of words, through the use of neural networks [25, 27].
The networks are trained by directly taking into account the word order and their co-occurrence, based on the assumption that words frequently appearing together in the sentences also share more statistical dependence. With the development of highly scalable continuous bag-of-words (CBOW) and skip-gram (SG) language models for word representation learning [17], the embedding models have been shown to obtain state-of-the-art performance on many traditional language tasks after training on large text data. More recently, the concept of embeddings has been extended beyond word representations to other applications outside of NLP domain. Researchers from the Web Search, E-commerce and Marketplace domains have quickly realized that just like one can train word embeddings by treating a sequence of words in a sentence as context, same can be done for training embeddings of user actions, e.g. items that were clicked or purchased [11, 18], queries and ads that were clicked [8, 9], by treating sequence of user actions as context. Ever since, we have seen embeddings being leveraged for various types of recommendations on the Web, including music recommendations [26], job search [13], app recommendations [21], movie recommendations [3, 7], etc. Furthermore, it has been shown that items which user interacted with can be leveraged to directly lean user embeddings in the same feature space as item embeddings, such that direct user-item recommendations can be made [6, 10, 11, 24, 27]. Alternative approach, specifically useful for cold-start recommendations, is to still to use text embeddings (e.g. ones publicly available at https://code.google.com/p/word2vec) and leverage item and or user meta data (e.g. title and description) to compute their embeddings [5, 14, 19, 28]. Finally, similar extensions of embedding approaches have been proposed for Social Network analysis, where random walks on graphs can be used to learn embeddings of nodes in graph structure [12, 20].
Embedding approaches have had a major impact in both academia and industry circles. Recent industry conference publications and talks show that they have been successfully deployed in various personalization, recommendation and ranking engines of major Web companies, such as Yahoo [8, 11, 29], Etsy [1], Criteo [18], Linkedin [15, 23], Tinder [16], Tumblr [10], Instacart [22], Facebook [28].
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5 CONCLUSION
We proposed a novel method for real-time personalization in Search Ranking at Airbnb. The method learns low-dimensional representations of home listings and users based on contextual co-occurrence in user click and booking sessions. To better leverage available search contexts, we incorporate concepts such as global context and explicit negative signals into the training procedure. We evaluated the proposed method in Similar Listing Recommendations and Search Ranking. After successful test on live search traffic both embedding applications were deployed to production.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2018 RealTimePersonalizationUsingEmb | Haibin Cheng Mihajlo Grbovic | Real-time Personalization Using Embeddings for Search Ranking at Airbnb | 2018 |