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arXiv · 1812.00158

Approximating Categorical Similarity in Sponsored Search Relevance

Hiba Ahsan·Rahul Agrawal
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Abstract

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.

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Hiba Ahsan, Rahul Agrawal. 2018-12-01. Approximating Categorical Similarity in Sponsored Search Relevance. https://arxiv.org/abs/1812.00158

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