In this paper, we propose an offline counterfactual policy estimation framework called Genie to optimize Sponsored Search Marketplace. Genie employs an open box simulation engine with click calibration model to compute the KPI impact of any modification to the system. From the experimental results on Bing traffic, we showed that Genie performs better than existing observational approaches that employs randomized experiments for traffic slices that have frequent policy updates. We also show that Genie can be used to tune completely new policies efficiently without creating risky randomized experiments due to cold start problem. As time of today, Genie hosts more than 10000 optimization jobs yearly which runs more than 30 Million processing node hours of big data jobs for Bing Ads. For the last 3 years, Genie has been proven to be the one of the major platforms to optimize Bing Ads Marketplace due to its reliability under frequent policy changes and its efficiency to minimize risks in real experiments.
Our focus in this paper is to propose efficient counterfactual estimator for practical tuning problems within the context of Sponsored Search System. In particular, we target on tuning scenarios where @math testing and existing observational approaches can not work efficiently. We split the causal graph for the sponsored search problem into input and system layer and apply different methodologies to predict outcomes. While machine learning methods gives very good results for predicting user click behaviors @cite_3 @cite_12 @cite_16 , we used log replay based to estimate the outcome of the sponsored search system. The reader could think of applying similar approaches for policy optimization problem when training data is very noisy due to frequent system changes or when it is not practical to create online experiment with the proposed modification.
@cite_16 @cite_12 @cite_3
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Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user's behaviors on ads yield high dependency on how the user behaved along with the past time, especially in terms of what queries she submitted, what ads she clicked or ignored, and how long she spent on the landing pages of clicked ads, etc. Inspired by these observations, we introduce a novel framework based on Recurrent Neural Networks (RNN). Compared to traditional methods, this framework directly models the dependency on user's sequential behaviors into the click prediction process through the recurrent structure in RNN. Large scale evaluations on the click-through logs from a commercial search engine demonstrate that our approach can significantly improve the click prediction accuracy, compared to sequence-independent approaches. This paper is concerned with the prediction of clicking an ad in sponsored search. The accurate prediction of user's click on an ad plays an important role in sponsored search, because it is widely used in both ranking and pricing of the ads. Previous work on click prediction usually takes a single ad as input, and ignores its relationship to the other ads shown in the same page. This independence assumption here, however, might not be valid in the real scenario. In this paper, we first perform an analysis on this issue by looking at the click-through rates (CTR) of the same ad, in the same position and for the same query, but surrounded by different ads. We found that in most cases the CTR varies largely, which suggests that the relationship between ads is really an important factor in predicting click probability. Furthermore, our investigation shows that the more similar the surrounding ads are to an ad, the lower the CTR of the ad is. Based on this observation, we design a continuous conditional random fields (CRF) based model for click prediction, which considers both the features of an ad and its similarity to the surrounding ads. We show that the model can be effectively learned using maximum likelihood estimation, and can also be efficiently inferred due to its closed form solution. Our experimental results on the click-through log from a commercial search engine show that the proposed model can predict clicks more accurately than previous independent models. To our best knowledge this is the first work that predicts ad clicks by considering the relationship between ads. In modern commercial search engines, the pay-per-click (PPC) advertising model is widely used in sponsored search. The search engines try to deliver ads which can produce greater click yields (the total number of clicks for the list of ads per impression). Therefore, predicting user clicks plays a critical role in sponsored search. The current ad-delivery strategy is a two-step approach which first predicts individual ad CTR for the given query and then selects the ads with higher predicted CTR. However, this strategy is naturally suboptimal and correlation between ads is often ignored under this strategy. The learning problem is focused on predicting individual performance rather than group performance which is the more important measurement. In this paper, we study click yield measurement in sponsored search and focus on the problem---predicting group performance (click yields) in sponsored search. To tackle all challenges in this problem---depth effects, interactive influence, cold start and sparseness of ad textual information---we first investigate several effects and propose a novel framework that could directly predict group performance for lists of ads. Our extensive experiments on a large-scale real-world dataset from a commercial search engine show that we achieve significant improvement by solving the sponsored search problem from the new perspective. Our methods noticeably outperform existing state-of-the-art approaches. Sponsored search is one of the major sources of revenue for search engines on the World Wide Web. It has been observed that while showing ads for every query maximizes short-term revenue, irrelevant ads lead to poor user experience and less revenue in the long-term. Hence, it is in search engines' interest to place ads only for queries that are likely to attract ad-clicks. Many algorithms for estimating query advertisability exist in literature, but most of these methods have been proposed for and tested on the frequent or "head" queries. Since query frequencies on search engine are known to be distributed as a power-law, this leaves a huge fraction of the queries uncovered. In this paper we focus on the more challenging problem of estimating query advertisability for infrequent or "tail" queries. These require fundamentally different methods than head queries: for e.g., tail queries are almost all unique and require the estimation method to be online and inexpensive. We show that previously proposed methods do not apply to tail queries, and when modified for our scenario they do not work well. Further, we give a simple, yet effective, approach, which estimates query advertisability using only the words present in the queries. We evaluate our approach on a real-world dataset consisting of search engine queries and user clicks. Our results show that our simple approach outperforms a more complex one based on regularized regression.