Location Aware Embedding for Geotargeting in Sponsored Search Advertising

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 (7)

• 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 \ref{['sec:location_web_search']}) form the main focus of this paper. The offline flow is applied only on local queries as tagged by the Local Intent Classifier Yi2009. 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.
• Figure 3: Graphical representations of four models illustrated on a sample session grbovic2016sigir.
• Figure 4: Average and Median gw2v Score by Editorial Grade
• Figure 5: Average NDCG results for $s2v$, $g2v$, $lw2v$ and $lw2v+$ models