Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to …
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48 results for “Display advertising”
Efficiently estimates conversion probability in online display advertising.
problem Estimating conversion probability with delays and large data sets.
method Compromise estimator combining logistic regression and joint model.
result Computational efficiency with less bias than previous methods.
Study predicts ad conversions based on URL embeddings.
problem Predicting user conversion likelihood from visited URLs.
method Introduced URL embedding models and evaluated conversion prediction models.
result Demonstrated effectiveness of URL embedding models for conversion prediction.
A PID-based feedback-control system improves multiple KPIs in RTB display advertising.
problem Challenges in simultaneously improving multiple KPIs in RTB campaigns.
method Sequential Control using PID-based feedback and importance metrics.
result Effective in simultaneously controlling multiple KPIs in both simulations and live traffic.
The paper tackles the issue of preferential attachment in targeted display advertising by developing domain-adaptation approaches.
problem Skewed distribution of data leads to preferential attachment towards high-budget partners.
method Develops domain-adaptation approaches to predict interested users for low-budget partners.
result Proposed approaches outperform other domain-adaptation methods across different points of campaigns.
Study proposes a time-aware model to predict user conversion intent.
problem Weak predictive signals from users not suitable for conversion prediction.
method Time-aware approach to model user activities and capture conversion intent signals.
result Approach outperforms other models on real-world datasets.
Adaptive display exposure improves e-commerce platform revenue.
problem Optimize ad display for better revenue and user experience.
method Constrained Markov Decision Process (psCMDP) and two-level reinforcement learning.
result Our approach increases platform revenue under constraints.
New attribution model boosts ad bidding efficiency.
problem Inefficiency of standard bidding policies in ad exchanges.
method Developed and applied an attribution model within the bidder.
result Average bid increased after incorporating attribution model.
New model for display advertising with stochastic and adversarial components.
problem Display advertising with stochastic and adversarial click-through-rates.
method Adversarial scaling model; two algorithms tested: action elimination and mirror descent.
result Two algorithms are robust to adversarial scaling.
A paper on optimizing ad bidding with multi-agent reinforcement learning.
problem Optimizing ad revenue and ROI in real-time display advertising.
method Multi-agent reinforcement learning with clustering and coordinated bidding.
result Cluster-based bidding outperforms single-agent and bandit approaches.
Dual learning algorithm addresses delayed conversions in CVR prediction.
problem Challenges in predicting conversion rate due to delayed feedback.
method Proposes two unbiased estimators and a dual learning algorithm.
result Demonstrates practical value of the proposed approach through empirical evaluations.
Advertisement (abbreviated ad) options are a recent development in online advertising. Simply, an ad option is a first look contract in which a publisher or search engine grants an advertiser a right but not obligation to enter into transactions to purchase impressions or clicks from a specific ad slot at a pre-specifi…
FwFMs improve CTR prediction with fewer parameters.
problem Predicting click-through rate in display advertising with multi-field categorical data.
method Field-weighted Factorization Machines (FwFMs) to model feature interactions efficiently.
result FwFMs achieve competitive performance with significantly fewer parameters than FFMs.
Paper addresses selection bias in online advertising auctions.
problem Selection bias affects auction truthfulness and advertiser profits.
method Theoretical analysis combined with multi-task learning.
result Selection bias can be significantly reduced using multi-task learning.
A method corrects feedback shift in predicting conversion rates with delayed feedback.
problem Delayed feedback leads to mislabeling of positive instances in training data.
method Uses importance weight approach to correct feedback shift.
result Proposed method outperforms existing methods in offline and online experiments.
Study estimates long-term effects of online advertising mechanisms on user behavior and revenue.
problem Estimating long-term treatment effects in online advertising systems.
method Modeling treatment effects as a stopped random walk, using experimental design, and statistical methods.
result Confidence intervals for long-term treatment effects constructed using statistical techniques.
Optimizes revenue and performance goals in e-commerce advertising with budget constraints.
problem Maximizing revenue and satisfying multiple performance goals in e-commerce advertising systems.
method Linear programming approach to optimize revenue and performance goals simultaneously.
result Our algorithm improves campaign performance and platform revenue effectively.
We consider online optimization in the 1-lookahead setting, where the objective does not decompose additively over the rounds of the online game. The resulting formulation enables us to deal with non-stationary and/or long-term constraints , which arise, for example, in online display advertising problems. We propose a…
Optimizes reserve prices for first-price auctions to maximize revenue.
problem Optimizing reserve prices for first-price auctions in display advertising.
method Gradient-based algorithm to adaptively update and optimize reserve prices based on bidder responsiveness to experimental shocks.
result Revenue optimization in first-price auctions can be decomposed into demand and bidding components, and techniques are introduced to reduce variance of each.
Adaptive algorithm for online evaluation of targeted audiences in advertising.
problem Determining the right match between advertising creatives and target audiences.
method Contextual bandit approach to address audience overlap and learn optimal display policies.
result The proposed method is more efficient than traditional split-testing methods.
Online advertising is an important and huge industry. Having knowledge of the website attributes can contribute greatly to business strategies for ad-targeting, content display, inventory purchase or revenue prediction. Classical inferences on users and sites impose challenge, because the data is voluminous, sparse, hi…
Develops a new bidding system to maximize advertiser profit.
problem Inaccurate prediction of ad lift-effect due to biased log data.
method Unbiased Lift-based Bidding System that predicts lift-effect from biased log data.
result Demonstrates superior and practical high-performing lift-based bidding strategy.
Enhanced ad event prediction boosts performance.
problem Improving ad performance metrics like CTR and CVR.
method New feature engineering approach for ad event prediction.
result Significantly outperforms alternative prediction algorithms.
The paper proposes a method to predict audio ad quality using acoustic features.
problem Improving user experience in online music streaming services by ensuring high quality audio advertisements.
method The paper proposes predicting audio ad quality using acoustic features and a proxy metric called Long Click Rate (LCR). A deep learning model is also introduced.
result The proposed deep learning model outperforms other models trained on hand-crafted features for audio ad quality prediction.
System filters inappropriate YouTube content for advertisers.
problem Inadequate detection of inappropriate content on YouTube ads.
method Proposes a system for identifying and filtering inappropriate content.
result Current countermeasures are ineffective in detecting inappropriate content.
This paper optimizes ad bids and daily budgets for multiple campaigns in pay-per-click advertising.
problem Optimizing ad bids and daily budgets for multiple campaigns in pay-per-click advertising.
method Formulated as a combinatorial semi-bandit problem, solved using Gaussian Processes and four algorithms.
result Regret upper bounded as O(sqrt{T}), where T is the time horizon.
A Q-learning approach optimizes RTB ad campaigns for mobile app installs.
problem Optimizing RTB ad campaigns for mobile app installs with delayed rewards.
method State space based policy trained via Q-learning algorithm to handle delayed install notifications.
result Significant increase in profit and number of efficient campaigns.
Deep neural nets predict click-through rates for sponsored search ads.
problem Predicting click-through rates for sponsored search ads.
method Two novel deep convolutional neural network approaches at character and word levels.
result Deep models significantly outperform baseline models and improve click-through rate prediction accuracy.
DNAMTA model improves media channel attribution accuracy.
problem Measuring the impact of each advertising channel in multi-channel marketing.
method Deep Neural Net with Attention (DNAMTA) model for multi-touch attribution.
result DNAMTA model outperforms existing methods in conversion prediction and media influence evaluation.
The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news. Recent approaches for off-policy evaluation and learning in these settings appear promising. …
Dimensionality reduction for click-through rate prediction: Dense versus sparse representationstat.ML
In online advertising, display ads are increasingly being placed based on real-time auctions where the advertiser who wins gets to serve the ad. This is called real-time bidding (RTB). In RTB, auctions have very tight time constraints on the order of 100ms. Therefore mechanisms for bidding intelligently such as clickth…
The paper learns optimal auction prices by matching supply and demand.
problem Predicting optimal prices for market clearing.
method Learning framework using auction data to optimize revenue.
result Learned prices outperform other models in auctions and markets.
We review a method for click-through rate prediction based on the work of Menon et al. [11], which combines collaborative filtering and matrix factorization with a side-information model and fuses the outputs to proper probabilities in [0,1]. In addition we provide details, both for the modeling as well as the experime…
New model for personalized online advertising with multi-user interaction.
problem Realistic online advertising scenarios with multiple users interacting simultaneously.
method Introduces Multi-User Contextual Cascading Bandit (MCCB) model and proposes UCBBP and AUCBBP algorithms.
result Proves UCBBP and AUCBBP achieve optimal regret bounds for multi-user context.
Clarifies the scope of 'reproducibility' in AI and ML.
problem Unclear meaning of 'reproducibility' in AI and ML.
method Analyzes the community's works on reproducibility.
result Identifies eight general topic areas of reproducibility.
New algorithm optimizes auction prices in real-time.
problem Maximizing revenue in online auctions with high frequency data.
method First real-time algorithm for online learning of monopoly prices.
result Achieves constant time and memory complexity for updates.
Study proposes an ad creative selection algorithm considering user fatigue.
problem Ad creative effectiveness varies with user exposure.
method Proposes an algorithm that considers user fatigue in ad selection.
result The proposed algorithm outperforms baseline algorithms in real-world testing.
DSPN predicts advertiser satisfaction and intent for e-commerce platforms.
problem Understanding advertiser intent and satisfaction for e-commerce platforms.
method Two-stage Deep Satisfaction Prediction Network (DSPN) that models intent and satisfaction.
result DSPN outperforms state-of-the-art baselines and predicts advertiser satisfaction accurately.
Neural nets predict user attention from mouse movements.
problem Predicting user attention from mouse cursor movements.
method Investigated different mouse movement representations and trained neural networks.
result Neural networks outperform handcrafted features for predicting user attention.
New method for causal inference in RTB advertising auctions.
problem Measuring the effectiveness of online advertising in RTB systems.
method Adapted Thompson sampling algorithm for causal inference.
result The method outperforms existing methods in estimating advertising effects.
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data. In this paper, we introduce an industrial strength solution with model named Large Scale Piece-wise Linear Model (LS-PLM). We formulate the learning problem with and regularizers, leadin…
Study optimal bidding strategies for digital ads targeting purchases and health campaigns.
problem Optimizing advertising strategies in digital channels.
method Continuous-time models encoding user behavior and auction mechanisms, semi-explicit formulas for optimal bidding.
result Semi-explicit formulas for optimal value and bidding policy for different types of advertising.
DIEN predicts CTR by evolving user interests from behavior data.
problem Capturing dynamic user interests for accurate CTR prediction.
method DIEN captures temporal and evolving user interests using interest extractor and evolving layers with attention mechanisms.
result DIEN significantly outperforms state-of-the-art solutions in CTR prediction.
New model uses symmetries and scaling laws to predict consumer advertising response.
problem Understanding consumer response to advertising efforts.
method Introduces a physics-based mathematical model to describe consumer response dynamics.
result The model better captures nonlinearities in advertising effects and provides new parameters for audience engagement.
This paper optimizes multi-channel sequential advertising to maximize cumulative revenue.
problem Maximizing cumulative revenue in multi-channel sequential advertising under a budget constraint.
method Formulated as a dynamic knapsack problem, proposed a bilevel optimization framework with action space reduction.
result Significantly improved cumulative revenue compared to state-of-the-art baselines.
The problem of detecting terms that can be interesting to the advertiser is considered. If a company has already bought some advertising terms which describe certain services, it is reasonable to find out the terms bought by competing companies. A part of them can be recommended as future advertising terms to the compa…
Advertising effects in Sznajd marketing modelcond-mat.stat-mech
The traditional Sznajd model, as well as its Ochrombel simplification for opinion spreading, are applied to marketing with the help of advertising. The larger the lattice is the smaller is the amount of advertising needed to convince the whole market
The paper models user-advertiser interactions using point processes.
problem Causal inference problems in user-advertiser interaction.
method Temporal marked point processes and neural point processes.
result Neural point processes as practical solutions.