Approximating Categorical Similarity in Sponsored Search Relevance
This work addresses relevance improvement for sponsored search engines, which is incremental as it builds on existing category-matching methods.
The paper tackles the problem of limited coverage in matching query and ad categories for sponsored search relevance by proposing a neural network approach using CLSM and tri-letter representations, resulting in a 5.23% improvement in AUC ROC offline and an 8.2% improvement in relevance from A/B testing.
Sponsored Search is a major source of revenue for web search engines. Since sponsored search follows a pay-per-click model, showing relevant ads for receiving clicks is crucial. Matching categories of a query and its ad candidates have been explored in modeling relevance of query-ad pairs. The approach involves matching cached categories of queries seen in the past to categories of candidate ads. Since queries have a heavy tail distribution, the approach has limited coverage. In this work, we propose approximating categorical similarity of a query-ad pairs using neural networks, particularly CLSM. Embedding of a query (or document) is generated using its tri-letter representation which allows coverage of tail queries. Offline experiments of incorporating this feature as opposed to using the categories directly show a 5.23% improvement in AUC ROC. A/B testing results show an improvement of 8.2% in relevance.