Sponsored search represents a major source of revenue for web search engines. The advertising model brings a unique possibility for advertisers to target direct user intent communicated through a search query, usually done 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 particularly challenging for advertisers to identify all 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 advance match 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, as part of this study, we open sourced query embeddings that can be used to advance the field.
@cite_21: The current boom of the Web is associated with the revenues originated from on-line advertising. While search-based advertising is dominant, the association of ads with a Web page (during user navigation) is becoming increasingly important. In this work, we study the problem of associating ads with a Web page, referred to as content-targeted advertising, from a computer science perspective. We assume that we have access to the text of the Web page, the keywords declared by an advertiser, and a text associated with the advertiser's business. Using no other information and operating in fully automatic fashion, we propose ten strategies for solving the problem and evaluate their effectiveness. Our methods indicate that a matching strategy that takes into account the semantics of the problem (referred to as AAK for "ads and keywords") can yield gains in average precision figures of 60 compared to a trivial vector-based strategy. Further, a more sophisticated impedance coupling strategy, which expands the text of the Web page to reduce vocabulary impedance with regard to an advertisement, can yield extra gains in average precision of 50 . These are first results. They suggest that great accuracy in content-targeted advertising can be attained with appropriate algorithms.
Alternative methods aim to address this issue by directly retrieving ads by calculating similarity between bag-of-words representation of queries and bag-of-words representation of ads obtained from ad title and description. Even though it reduces sparsity, this approach does not completely solve the vocabulary mismatch problem @cite_21 and may even introduce additional issues, especially in the case of templated ad creatives where majority of phrases in titles and descriptions have little to do with the advertised product (e.g., free shipping", best prices", buy now").