Real-time Personalization using Embeddings for Search Ranking at Airbnb

By Mihajlo Grbovic, Haibin Cheng. Knowledge Discovery and Data Mining, 2018

323 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Information Systems, Marketing

View Paper: https://www.semanticscholar.org/paper/2553596e3b24444d309ae461da50370d58f1b807

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Kang G. Shin
2. Hai Jin
3. Yupeng Hou
4. Hongyu Lu
5. Wayne Xin Zhao
6. Ruobing Xie
7. Julian McAuley
8. Wei Li

In Information Systems

1. Yupeng Hou
2. Wayne Xin Zhao
3. Hongyu Lu
4. Wenwu Ou
5. Zihan Lin
6. Huan Zhao
7. Ruobing Xie
8. Junjie Zhang

In Marketing

1. Wenwu Ou
2. Yupeng Hou
3. Mohan Kankanhalli
4. Wayne Xin Zhao
5. Julian McAuley
6. Huan Zhao
7. Yongfeng Zhang
8. Philip S. Yu

Citations in the corpus

• Robust Federated Recommendation System (2020, background): “It is imperative for recommendation systems to handle real-time personalization [14].”
• Milvus: A Purpose-Built Vector Data Management System (2021, background): “In particular, a recent popular approach in recommender systems is called vector embedding that converts an item to a feature vector (such as item2vec [11], word2vec [52], doc2vec [37], graph2vec [26]) and provides recommendations via � nding similar vectors [13, 15, 25, 51].”
• Federated User Representation Learning (2019, background): “Collaborative personalization, like learning user embeddings jointly with the task, is a powerful way to improve accuracy of neural-network-based models by adapting the model to each user’s behavior [8, 23, 14, 11, 21, 31].”
• Large Language Models are Zero-Shot Rankers for Recommender Systems (2023, methodology): “5-turbo to (1) LLMs that have not been instruction-tuned, like LLaMA [28], and (2) LLMs that have been fine-tuned on instructions, including Vicuna [3] and text-davinci-003 2 , and then instruct these LLMs to perform the ranking task.”
• Behavior sequence transformer for e-commerce recommendation in Alibaba (2019, background): “During the last decade, Recommender Systems (RSs) have been the most popular application in industry, and in the past five years, deep learning based methods have been widely used in industrial RSs, e.g., Google [2, 3] and Airbnb [5].”