Adobe, Inc.

Chen Dong

Zhenyu Yan

Abhishek Pani

Yuan Yuan

Xiang Wu

Fnu Arava Venkata Kesava Sai Kumar

Ning Li

Yiwen Sun

Xiaojing Dong

Pinak Panigrahi

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.

Identifying touchpoint contribution utilizing a touchpoint attribution attention neural network

The present disclosure includes systems, methods, and non-transitory computer readable media that train and utilize multi-phase learning models to predict performance during digital content campaigns and provide digital content to client devices in a real-time bidding environment. In particular, one or more embodiments leverage organizational structure of digital content campaigns to train two learning models, utilizing different data sources, to predict performance, generate bid responses, and provide digital content to client devices. For example, the disclosed systems can train a first performance learning model in an offline mode utilizing parent-level historical data. Then, in an online mode, the disclosed systems can train a second performance learning model utilizing child-level historical data and utilize the first performance learning model and the second performance learning model to generate bid responses and bid amounts in a real-time bidding environment.

Training and utilizing multi-phase learning models to provide digital content to client devices in a real-time digital bidding environment

Utilizing a touchpoint attribution attention neural network to identify significant touchpoints and measure touchpoint contribution in multichannel, multi-touch digital content campaigns

A bid management system generates estimated performance metrics at the bid unit level to facilitate bid optimization. The bid management system includes a hierarchical feature selection and prediction approach. Feature selection is performed by aggregating historical performance metrics to a higher hierarchical level and testing features for statistical significance. Features for which a significance level satisfies a significance threshold are selected for prediction analysis. The prediction analysis uses a statistical model based on selected features to generate estimated performance metrics at the bid unit level. In some implementations, the prediction analysis uses a hierarchical Bayesian smoothing method in which estimated performance metrics are calculated at the bid unit level using a posterior probability distribution derived from a prior probability distribution determined based on aggregated performance metrics and a likelihood function that takes into account historical performance metrics from the bid unit level based on the selected features.

Hierarchical feature selection and predictive modeling for estimating performance metrics

Dynamic Hierarchical Empirical Bayes techniques and systems are described that are implemented to control output of digital content. In one example, a system identifies splitting variables included in data. An amount of loss is then determined for each of the identified splitting variables by the system using a loss function. Based on the determined amounts of loss, the system selects at least one splitting variable from the plurality of splitting variables that are to be used to partition data in a respective node, e.g., a parent node to form a plurality of child nodes. The system, for instance, may select the splitting variable that minimizes the cost, i.e., has the lowest amount of cost. The selected splitting variable is then employed by the system to generate at least one hierarchical level of the hierarchical structure of the statistical model by partitioning data from the parent node into respective child nodes.

Dynamic Hierarchical Empirical Bayes and digital content control

In various implementations, analytics data is received that indicates performance of bid targets for historical bids made in one or more content delivery auctions. Baseline prediction models are maintained for the bid targets. The baseline prediction models use the analytics data to predict performance of the bid targets in one or more future instances of at least one content delivery auction. A presentation context factor model is maintained that provides an adjustment factor that quantifies a contribution of a subset of a plurality of presentation context factors associated with the bid targets to performance of the bid targets based on predicted values from the baseline prediction models. A contextual predicted value is computed using the adjustment factor for the subset of the plurality of presentation context factors. A performance prediction is transmitted to a user device and is based on at least the contextual predicted value.

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Chen Dong