document: Catching the drift : learning broad matches from clickthrough data Identifying similar keywords , known as broad matches , is an important task in online advertising that has become a standard feature on all major keyword advertising platforms . Effective broad matching leads to improvements in both relevance and monetization , while increasing advertisers ' reach and making campaign management easier . In this paper , we present a learning-based approach to broad matching that is based on exploiting implicit feedback in the form of advertisement clickthrough logs . Our method can utilize arbitrary similarity functions by incorporating them as features . We present an online learning algorithm , Amnesiac Averaged Perceptron , that is highly efficient yet able to quickly adjust to the rapidly-changing distributions of bidded keywords , advertisements and user behavior . Experimental results obtained from ( 1 ) historical logs and ( 2 ) live trials on a large-scale advertising platform demonstrate the effectiveness of the proposed algorithm and the overall success of our approach in identifying high-quality broad match mappings .
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