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Abstract. The critical task of predicting clicks on search advertisements is typically addressed by learning from historical click data. When enough history is observed for a given query-ad pair, future clicks can be accurately modeled. However, based on the empirical distribution of queries, sufficient historical information is unavailable for many queryad pairs. The sparsity of data for new and rare queries makes it difficult to accurately estimate clicks for a significant portion of typical search engine traffic. In this paper we provide analysis to motivate modeling approaches that can reduce the sparsity of the large space of user search queries. We then propose methods to improve click and relevance models for sponsored search by mining click behavior for partial user queries. We aggregate click history for individual query words, as well as for phrases extracted with a CRF model. The new models show significant improvement in clicks and revenue compared to state-of-the-art baselines trained on several months of query logs. Results are reported on live traffic of a commercial search engine, in addition to results from offline evaluation.

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We present a document expansion approach that uses Conditional Random Field (CRF) segmentation to automatically extract salient phrases from ad titles. We then supplement the ad document with query segments that are probable translations of the document phrases, as learned from a large commercial search engine's click logs. Our approach provides a significant improvement in DCG and interpolated precision and recall on a large set of human labeled query-ad pairs.