Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising

TL;DR

This paper introduces a scalable semantic embedding approach for matching search queries to ads, improving relevance and revenue in sponsored search through large-scale data-driven learning and addressing cold-start issues.

Contribution

We propose a novel distributed embedding learning method for large-scale sponsored search, incorporating contextual signals and addressing cold-start problems, with open-source embeddings for research use.

Findings

Significant improvement in relevance and coverage over baselines.

Enhanced incremental revenue in online experiments.

Effective handling of cold-start for new ads and queries.

Abstract

Sponsored search represents a major source of revenue for web search engines. This popular advertising model brings a unique possibility for advertisers to target users' immediate intent communicated through a search query, usually by displaying their ads alongside organic search results for queries deemed relevant to their products or services. However, due to a large number of unique queries it is challenging for advertisers to identify all such relevant queries. For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on. We present a novel advanced matching approach based on the idea of semantic embeddings of queries and ads. The embeddings were learned using a large data set of user search sessions, consisting of search queries, clicked ads and search links, while utilizing contextual information such as dwell time and skipped ads. To address the large-scale nature of our problem, both in terms of data and vocabulary size, we propose a novel distributed algorithm for training of the embeddings. Finally, we present an approach for overcoming a cold-start problem associated with new ads and queries. We report results of editorial evaluation and online tests on actual search traffic. The results show that our approach significantly outperforms baselines in terms of relevance, coverage, and incremental revenue. Lastly, we open-source learned query embeddings to be used by researchers in computational advertising and related fields.