Combining Powers of Two Predictors in Optimizing Real-Time Bidding Strategy under Constrained Budget

Lin, Chi-Chun; Chuang, Kun-Ta; Wu, Wush; Chen, Ming-Syan

doi:10.1145/2983323.2983656

Cited by 19 publications

(17 citation statements)

References 9 publications

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“…For the future work, we plan to incorporate the proposed bid landscape forecasting model into bid optimization for profit maximization [7,19,26] in real-time bidding advertising.…”

Section: Discussionmentioning

confidence: 99%

“…Bid Landscape Forecasting. As is discussed in the above section, bid landscape forecasting has become an important component in RTB advertising and drawn much attention in recent works [19,26,27].…”

Section: Related Workmentioning

confidence: 99%

“…One of the challenges is that it is infeasible to model the bidding strategy of each competing bidder since the participating advertisers do not interact with each other [29]. It is natural to model the market as a whole and regard the market price as a variable z [19,29,39]. Recall that the market price is the second highest bid price among all the bidders in the second-price auction, i.e., the highest bid price from the competitors in the view of the auction winner.…”

Section: Preliminariesmentioning

confidence: 99%

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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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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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“…For the future work, we plan to incorporate the proposed bid landscape forecasting model into bid optimization for profit maximization [7,19,26] in real-time bidding advertising.…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Third, based on the estimated CTR and cost of the ad display opportunity, we will seek for the optimal bidding function along with other considerations including the campaign budget and the auction volume etc. [19], [22], [30], [47], [48].…”

mentioning

confidence: 99%

“…On the other hand, the bidding strategy also influences the utility estimation and cost prediction modules. In [22], the authors proved the advantage for bidding effectiveness of combining the two optimization models for both CTR and the winning price under budget constraints. As a consequence, the natural idea is to solve the three main challenges altogether and derive a comprehensive methodology to jointly optimize the bidding performance.…”

mentioning

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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