Publication: RespRate-LSH : response rate estimation using LSH
Abstract
Arama motoru kullanıcılarına yönelik reklamcılık, çevrimiçi reklamcılığın birincil aracıdır. Bu reklamcılık, arama motorları için en büyük gelir kaynağıdır. Performansa dayalı reklamcılık, hem reklam verenler hem de arama motoru için gereklidir. Arama bazlı reklamcılığındaki kullanıcı yanıt oranı, linke tıklama veya dönüşüm gibi istenen bir kullanıcı işleminin gözlemlenen oranını ifade etmektedir. Yanıt oranını tahmin etmek için, yerele duyarlı hashing (LSH) kullanarak RespRate-LSH adlı komşu tabanlı bir veri ekstrapolasyon yöntemi oluşturduk. Hedef yanıt oranı, LSH aracılığıyla tanımlanan yakın komşuların yanıt oranlarının ağırlıklı ortalaması olarak tahmin edilir. RespRate-LSH'nin hiperparametreleri ayrıntılı olarak incelendi ve deneysel performansı geleneksel makine öğrenme yöntemleri ve derin sinir ağları ile karşılaştırıldı. RespRate-LSH örnek bir performans gösterdi.
Advertising to search engine users is a primary medium of online advertising. It is the largest source of revenue for search engines. Performance-driven advertising is essential for advertisers and search engines alike. The user response rate in search advertising refers to the observed rate of a desired user action such as click-through or conversion. To estimate the response rate, we built a near-neighbor based data extrapolation method called RespRate-LSH using locality sensitive hashing. The target response rate is estimated as the weighted average of the response rates of near neighbors identified via LSH. The hyper-parameters of RespRate-LSH were studied in detail, and its empirical performance was compared with traditional machine learning methods and with deep neural networks. RespRate-LSH showed exemplary performance.
Advertising to search engine users is a primary medium of online advertising. It is the largest source of revenue for search engines. Performance-driven advertising is essential for advertisers and search engines alike. The user response rate in search advertising refers to the observed rate of a desired user action such as click-through or conversion. To estimate the response rate, we built a near-neighbor based data extrapolation method called RespRate-LSH using locality sensitive hashing. The target response rate is estimated as the weighted average of the response rates of near neighbors identified via LSH. The hyper-parameters of RespRate-LSH were studied in detail, and its empirical performance was compared with traditional machine learning methods and with deep neural networks. RespRate-LSH showed exemplary performance.