Location Aware Embedding for Geotargeting in Sponsored Search Advertising

Jelena Gligorijevic; Djordje Gligorijevic; Aravindan Raghuveer; Mihajlo Grbovic; Zoran Obradovic

cs.IRMar 14, 2026

Location Aware Embedding for Geotargeting in Sponsored Search Advertising

Authors: Jelena Gligorijevic, Djordje Gligorijevic, Aravindan Raghuveer, Mihajlo Grbovic, Zoran Obradovic

Organizations: Yahoo! Research · Temple University

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.

Figures & tables

Figure 1. Tail is predominant in local intent queries. Queries that occur less than 5 times per month account for close to 45% of total local query volume

Figure 2. System Architecture: Our system consists of two parts: The Offline Embeddings Learning Flow and the Online Query to Ad Matching Flow . In the Offline Embeddings Learning Flow we use session query and click logs to learn low dimensional embeddings for queries, ads, location and semantic text fragments in the same vector space. In the Online Query to Ad Matching Flow we use the learned embeddings to retrieve top k ads for an incoming query Q. The worLd2vec embedding learning step in the offline flow and the worLd2vec Query Vector Composition step in the online flow (both described in Section 3 ) form the main focus of this paper. The offline flow is applied only on local queries as tagged by the Local Intent Classifier ( Yi et al., 2009 ) . The vectors learned by the worLd2vec training step are stored in a embedding database and used later in the Online Query to Ad Matching Flow . The query vector is at the time of searving used to retrieve the top k nearest ad vectors from the ads database.

Panel of Figure 3 — Figure 3. Graphical representations of four models illustrated on a sample session ( Grbovic et al., 2016 ) .

			precision@K
	model	task	1	2	3	5	10
implicit	$s2v$	q2ad	0.204	0.369	0.490	0.550	0.582
	$gw2v_{w}$	q2ad	0.384	0.481	0.575	0.610	0.642
	$gw2v_{p}$	q2ad	0.409	0.530	0.628	0.669	0.711
explicit	$s2v$	q2ad	0.062	0.075	0.086	0.094	0.111
	$gw2v_{w}$	q2ad	0.111	0.136	0.147	0.158	0.174

Table 1. precision@K for s2v and gw2v algorithms (for poi and woeid location representations) trained on Web search sessions with local intent (implicit and explicit) queries

Query	Cosine Similarity
Universal Islands of Adventure	0.959
Disney MGM studios Orlando	0.958
Universal Studios Orlando discount tickets	0.956
MGM Studios Orlando	0.954
Islands of Adventure Orlando	0.952
Harry Potter Universal Studios	0.951

Table 2. Top 10 nearest search queries to the point of interest “Universal Studios Florida”

Figure 4. Average and Median gw2v Score by Editorial Grade

Figure 5. Average NDCG results for $s2v$ , $g2v$ , $lw2v$ and $lw2v+$ models

			precision@K
	model	task	1	2	3	5	10
implicit	lw2v	q2a	0.369	0.472	0.544	0.580	0.663
	lw2v	(q + l)2a	0.259	0.375	0.506	0.552	0.594
	lw2v +	q2a	0.375	0.485	0.553	0.593	0.656
	lw2v +	(q + l)2a	0.388	0.492	0.573	0.620	0.672
	lw2vC +	(q_s_l)2a	0.392	0.497	0.588	0.626	0.689

Table 3. precision@K on query2ad , (query&location)2ad , qualifier_subject_location2ad , subject_location2ad , (subject+location)2ad and qualifierType_subject_location2ad tasks for 3 local-worLd2vec ( l2v , l2v+ and l2vCRF+ ) algorithms trained on Web search sessions with local intent (implicit and explicit) queries or queries semantic fragments

Panel of Figure 6 — Figure 6. Individual keywords for top 100 relevant queries to the search of ATL airport, obtained by lw2v (a) and lw2v+ (b)

Tag	Precision	Recall
Organization Name	0.89	0.86
Business Category	0.7	0.74
Location:State	0.96	0.85
Location:City	0.92	0.93
Location:ZipCode	1.0	0.97

Table 4. Performance of the local CRF model

Figure 7. Graphical representations of the lw2vCRF+ model illustrated on a sample session. The representations of semantic query fragments are learned as a sum composition.

Query Composition	Cosine Similarity
woeid_249115	0.953
hotels_woeid_249115	0.947
hotel_woeid_249115	0.921
superlative_hotels_woeid_249115	0.906
hotel_woeid_12589342	0.901

Table 5. The closest semantic query fragments to the summation vector of subject ’ hotels ’ and location ’ woeid_2459115 ’ ( New York )

Online Advertising

Online advertising studies how digital ads are targeted, ranked, priced, measured, and delivered while balancing budgets, business goals, and user interests.

See all 87 papers

Much of Geospatial Web Search Is Beyond Traditional GIS
May 11, 2026Ilya Ilyankou, Stefano Cavazzi, James HaworthInformation Retrieval
SpaceTimeLab, Department of Civil, Environmental, and Geomatic Engineering, UCL, London, UK · Ordnance Survey, Southampton, UK
What's in an Earth Embedding? An Explainability Analysis of Location Encoders
Jun 23, 2026Livia Betti, Sebastian Ricke, Ivica Obadic +2Implicit Neural Representations Geospatial Representation Learning
University of Colorado Boulder · Technical University of Munich · Munich Center for Machine Learning
PlaceSeek: Human-Centered Geospatial Retrieval of Urban Outdoor Places via Semantic Grounding and Affective Alignment
Aug 25, 2026Ziqi Cui, Shangyu LouInformation Retrieval Image Retrieval
University of California, Santa Barbara & San Diego State University California, USA

May 11, 2026J/K move · Enter open · S save

Much of Geospatial Web Search Is Beyond Traditional GIS

Ilya Ilyankou, Stefano Cavazzi, James Haworth

SpaceTimeLab, Department of Civil, Environmental, and Geomatic Engineering, UCL, London, UK · Ordnance Survey, Southampton, UK

Information Retrieval

Open paper AskPDF arXiv

Web search queries concern place far more often than existing labelling schemes suggest, yet the landscape of geospatial web search queries - what people ask of place, and how often - remains poorly characterised at scale. We apply dense sentence embeddings, a lightweight SetFit classifier, and density-based clustering to the full MS MARCO corpus of 1.01 million real Bing queries without prior filtering for toponyms or spatial keywords, identifying 181,827 geospatial queries (18.0%), nearly threefold the 6.17% labelled as Location in the original annotations. The resulting taxonomy of 88 query categories reveals that geospatial web search is dominated by transactional and practical lookups: costs and prices alone account for 15.3% of geospatial queries, nearly twice the size of the entire physical geography theme. Much of this activity - costs, opening hours, contact details, weather, travel recommendations - falls outside the scope of what traditional GIS and knowledge graphs are built to serve. The categories vary substantially in the kind of answer they admit, from deterministic lookups answerable from spatial databases or knowledge graphs to evaluative or temporally volatile queries that require generative or real-time systems. We discuss implications for hybrid retrieval architectures and for benchmarks of geographic reasoning in large language models. We openly release the labelled dataset, classifier, and taxonomy.

Abstract

Online Advertising

Explore similar work

Much of Geospatial Web Search Is Beyond Traditional GIS

What's in an Earth Embedding? An Explainability Analysis of Location Encoders

PlaceSeek: Human-Centered Geospatial Retrieval of Urban Outdoor Places via Semantic Grounding and Affective Alignment