Median of the<i>p</i>Value Under the Alternative Hypothesis

The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. Specifically, we utilize a recurrent neural network to flexibly model the conditional winning probability w.r.t. each bid price. Then we conduct the bid landscape forecasting through probability chain rule with strict mathematical derivations. And, in an end-to-end manner, we optimize the model by minimizing two negative likelihood losses with comprehensive motivations. Without any specific assumption for the distribution form of bid landscape, our model shows great advantages over previous works on fitting various sophisticated market price distributions. In the experiments over two large-scale real-world datasets, our model significantly outperforms the state-of-the-art solutions under various metrics.

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“…the baseline models. Specifically, we deploy a Mann-Whitney U test [21] under C-index metric, and a t-test [2] under ANLP metric.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Deep Landscape Forecasting for Real-time Bidding Advertising

Ren

Qin

Zheng

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…Finally, we conduct the significance test to verify the statistical significance of the performance improvement of our model against the baseline models. Specifically, we deploy a MannWhitney U test [36] under AUC metric, and a t-test [4] under Log-loss metric.…”

Section: Experiments Flowmentioning

confidence: 99%

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

Ren

Qin

et al. 2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With twodecade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.

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“…In the second scenario, we evaluate different bidding strategies under budget constraints. Here we test 4 solutions: 5. It has been shown that AUC is equal to the probability that a regressor correctly ranks a randomly chosen positive example higher than a randomly chosen negative one.…”

Section: Compared Settingsmentioning

confidence: 99%

“…For the RMSE metric, we tested p-values [5] of the predicted CTR values across all the models, which is illustrated in find that the improvement of EU and RR model over linear regression models are significant, either for the BM(MKT) model against all the baselines. We also report the results of p-values under ANLP metric, which is shown in Table 13.…”

Section: Appendix F Significance Testmentioning

confidence: 99%

Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

Ren

Zhang

Chang

et al. 2018

IEEE Trans. Knowl. Data Eng.

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Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose Bidding Machine, a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine.

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Median of thepValue Under the Alternative Hypothesis

Cited by 65 publications

References 7 publications

Deep Landscape Forecasting for Real-time Bidding Advertising

Deep Landscape Forecasting for Real-time Bidding Advertising

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

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