# BiListing: Modality Alignment for Listings

## TL;DR

BiListing introduces a novel modality alignment approach that combines text and images into unified embeddings for Airbnb listings, enhancing search and recommendation capabilities with zero-shot and cross-modality features.

## Contribution

The paper presents BiListing, a new method leveraging large-language and pretrained models to align text and images into a single embedding, improving search and cold start handling.

## Key findings

- Achieved 0.425% NDCG gain in search ranking.
- Enabled efficient zero-shot inventory search.
- Drove tens of millions in incremental revenue.

## Abstract

Airbnb is a leader in offering travel accommodations. Airbnb has historically relied on structured data to understand, rank, and recommend listings to guests due to the limited capabilities and associated complexity arising from extracting meaningful information from text and images. With the rise of representation learning, leveraging rich information from text and photos has become easier. A popular approach has been to create embeddings for text documents and images to enable use cases of computing similarities between listings or using embeddings as features in an ML model. However, an Airbnb listing has diverse unstructured data: multiple images, various unstructured text documents such as title, description, and reviews, making this approach challenging. Specifically, it is a non-trivial task to combine multiple embeddings of different pieces of information to reach a single representation. This paper proposes BiListing, for Bimodal Listing, an approach to align text and photos of a listing by leveraging large-language models and pretrained language-image models. The BiListing approach has several favorable characteristics: capturing unstructured data into a single embedding vector per listing and modality, enabling zero-shot capability to search inventory efficiently in user-friendly semantics, overcoming the cold start problem, and enabling listing-to-listing search along a single modality, or both. We conducted offline and online tests to leverage the BiListing embeddings in the Airbnb search ranking model, and successfully deployed it in production, achieved 0.425% of NDCB gain, and drove tens of millions in incremental revenue.