Location Aware Embedding for Geotargeting in Sponsored Search Advertising

TL;DR

This paper introduces a neural embedding framework that jointly represents user queries and locations in a low-dimensional space, enhancing ad relevance and query understanding in location-aware sponsored search advertising.

Contribution

The study presents a novel neural embedding approach that effectively captures interactions between query intent and location, improving relevance scores over existing methods.

Findings

Improved ad ranking accuracy.

Enhanced query-ad relevance scores.

Effective representation of user location and query interaction.

Abstract

Web search has become an inevitable part of everyday life. Improving and monetizing web search has been a focus of major Internet players. Understanding the context of web search query is an important aspect of this task as it represents unobserved facts that add meaning to an otherwise incomplete query.The context of a query consists of user's location, local time, search history, behavioral segments, installed apps on their phone and so on. Queries that either explicitly use location context (eg: "best hotels in New York City") or implicitly refer to the user's physical location (e.g. "coffee shops near me") are becoming increasingly common on mobile devices. Understanding and representing the user's interest location and/or physical location is essential for providing a relevant user experience. In this study, we developed a simple and powerful neural embedding based framework to represent a user's query and their location in a single low-dimensional space. We show that this representation is able to capture the subtle interactions between the user's query intent and query/physical location, while improving the ad ranking and query-ad relevance scores over other location-unaware approaches and location-aware approaches.