Managing Diversity in Airbnb Search

03/31/2020
by   Mustafa Abdool, et al.
0

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.

READ FULL TEXT

page 2

page 4

research
09/10/2023

Personalized Search Via Neural Contextual Semantic Relevance Ranking

Existing neural relevance models do not give enough consideration for qu...
research
06/14/2022

Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search

Improving the quality of search results can significantly enhance users ...
research
10/22/2018

Applying Deep Learning To Airbnb Search

The application to search ranking is one of the biggest machine learning...
research
04/30/2019

Personalized Ranking in eCommerce Search

We address the problem of personalization in the context of eCommerce se...
research
10/27/2020

Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics

The way pages are ranked in search results influences whether the users ...
research
01/26/2017

Match-Tensor: a Deep Relevance Model for Search

The application of Deep Neural Networks for ranking in search engines ma...

Please sign up or login with your details

Forgot password? Click here to reset