Response prediction using collaborative filtering with hierarchies and side-information

Menon, Aditya Krishna; Chitrapura, Krishna-Prasad; Garg, Sachin; Agarwal, Deepak; Kota, Nagaraj

doi:10.1145/2020408.2020436

Cited by 123 publications

(119 citation statements)

References 18 publications

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“…The user response prediction, such as the click-through rate (CTR) estimation or the conversion rate (CVR) estimation, has become a core research problem in real-time display advertising [14,18,26]. The response prediction is a probability estimation task [19] which models the interest of users towards the content of publishers or the ads, and is used to derive the budget allocation of the advertisers [23]. Typically, the response prediction problem is formulated as a regression problem with prediction likelihood as the training objective [23,9,1,21].…”

Section: Related Workmentioning

confidence: 99%

“…Typically, the response prediction problem is formulated as a regression problem with prediction likelihood as the training objective [23,9,1,21]. From the methodology view, linear models such as logistic regression [14] and non-linear models such as tree-based model [10] and factorization machines [19,21] are commonly used. Other variants include Bayesian probit regression [9], FTRFL [24] in factorization machine, and convolutional neural network learning framework [17].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

User Response Learning for Directly Optimizing Campaign Performance in Display Advertising

Ren

Zhang

Rong

et al. 2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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Learning and predicting user responses, such as clicks and conversions, are crucial for many Internet-based businesses including web search, e-commerce, and online advertising. Typically, a user response model is established by optimizing the prediction accuracy, e.g., minimizing the error between the prediction and the ground truth user response. However, in many practical cases, predicting user responses is only part of a rather larger predictive or optimization task, where on one hand, the accuracy of a user response prediction determines the final (expected) utility to be optimized, but on the other hand, its learning may also be influenced from the follow-up stochastic process. It is, thus, of great interest to optimize the entire process as a whole rather than treat them independently or sequentially. In this paper, we take real-time display advertising as an example, where the predicted user's ad click-through rate (CTR) is employed to calculate a bid for an ad impression in the second price auction. We reformulate a common logistic regression CTR model by putting it back into its subsequent bidding context: rather than minimizing the prediction error, the model parameters are learned directly by optimizing campaign profit. The gradient update resulted from our formulations naturally fine-tunes the cases where the market competition is high, leading to a more costeffective bidding. Our experiments demonstrate that, while maintaining comparable CTR prediction accuracy, our proposed user response learning leads to campaign profit gains as much as 78.2% for offline test and 25.5% for online A/B test over strong baselines.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

User Response Learning for Directly Optimizing Campaign Performance in Display Advertising

Ren

Zhang

Rong

et al. 2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

View full text Add to dashboard Cite

show abstract

“…However, the individual features may have low correlation with user's click/convert intention. In [15,17], authors use feature pairs of pages and ads to learn latent factors through matrix factorization (MF). Furthermore, in [23], the author investigate the advantage of feed forward neural networks in discovering the latent structure in the high dimensional feature space.…”

Section: Related Workmentioning

confidence: 99%

Behavior profiling for mobile advertising

State

Brorsson³

et al. 2016

Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

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Behavioral and targeted profiling of users is an important task in marketing and in the advertising industry. Being able to match a given user profile to an advertising that leads to effective purchases is challenging because of a very tiny proportion of users willing to purchase goods and thus monetize the advertising. With such proportions being less than one percent of the overall user population, efficient feature extraction and modeling techniques are required in order to capture and recognize the potential consumers. This paper proposes a new approach for modeling the observed behavior in a mobile advertising platform, where time related features are correlated with additional system level and campaign related performance statistics. We capture the temporal behavior with Hawkes processes and use the estimated parameters as additional features for predicting if a given user profile will be a revenue generating customer.

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“…Menon et al [2] proposed the maximum likelihood algorithm to estimate the parameters of the CTR probabilistic model. But this model can only be applied to the existing advertisements rather than the new advertisements.…”

Section: Introductionmentioning

confidence: 99%

Advertisement Click-Through Rate Prediction Based on the Weighted-ELM and Adaboost Algorithm

Zhang

Xiao

2017

Scientific Programming

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Accurate click-through rate (CTR) prediction can not only improve the advertisement company's reputation and revenue, but also help the advertisers to optimize the advertising performance. There are two main unsolved problems of the CTR prediction: low prediction accuracy due to the imbalanced distribution of the advertising data and the lack of the real-time advertisement bidding implementation. In this paper, we will develop a novel online CTR prediction approach by incorporating the real-time bidding (RTB) advertising by the following strategies: user profile system is constructed from the historical data of the RTB advertising to describe the user features, the historical CTR features, the ID features, and the other numerical features. A novel CTR prediction approach is presented to address the imbalanced learning sample distribution by integrating the Weighted-ELM (WELM) and the Adaboost algorithm. Compared to the commonly used algorithms, the proposed approach can improve the CTR significantly.

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Response prediction using collaborative filtering with hierarchies and side-information

Cited by 123 publications

References 18 publications

User Response Learning for Directly Optimizing Campaign Performance in Display Advertising

User Response Learning for Directly Optimizing Campaign Performance in Display Advertising

Behavior profiling for mobile advertising

Advertisement Click-Through Rate Prediction Based on the Weighted-ELM and Adaboost Algorithm

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