Location Aware Embedding for Geotargeting in Sponsored Search Advertising

2026-03-14

AI Summary

This work addresses the critical role of fine-grained associations between user queries and geographic locations—whether explicitly stated or implicitly inferred—in enhancing ad relevance within sponsored search advertising. The authors propose a novel neural embedding framework that jointly maps queries and location signals into a unified low-dimensional vector space, enabling a cohesive representation of geographic context. This approach effectively captures complex interactions between user intent and spatial information, substantially outperforming conventional strategies that either ignore location entirely or employ simplistic concatenation of location features. Experimental results demonstrate significant improvements in both ad ranking performance and query–ad relevance scoring, thereby validating the effectiveness and novelty of location-aware embeddings in the context of mobile search advertising.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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.

Problem

• geotargeting
• sponsored search advertising
• location context
• query intent
• user location

Innovation

• location-aware embedding
• geotargeting
• sponsored search advertising
• neural embedding
• query-location interaction