Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) the attribution of these conversions to advertising events (such as clic…
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48 results for “click probability”
A search engine recommends to the user a list of web pages. The user examines this list, from the first page to the last, and clicks on all attractive pages until the user is satisfied. This behavior of the user can be described by the dependent click model (DCM). We propose DCM bandits, an online learning variant of t…
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…
A new model predicts conversion rates by analyzing post-click actions.
problem Challenges in predicting conversion rates due to sample selection bias and data sparsity.
method Post-click behavior decomposition and multi-task learning.
result The model effectively addresses sample selection bias and data sparsity issues.
New model for online ranking with feature analysis.
problem Online ranking with click probability and feature vectors.
method Linear function of feature vector and unknown parameter; novel algorithm with improved regret.
result Improved algorithm handling large number of items with reduced dependence on item count.
The probability that a user will click a search result depends both on its relevance and its position on the results page. The position based model explains this behavior by ascribing to every item an attraction probability, and to every position an examination probability. To be clicked, a result must be both attracti…
Estimates conversion probabilities from click sequences with privacy constraints.
problem Training models in advertising with limited direct click-conversion links.
method Formalizes learning from attribution sets, constructs unbiased estimator, applies Empirical Risk Minimization.
result Empirical Risk Minimization achieves generalization guarantees and robustness against prior errors.
Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rel…
A new dataset tracks user interactions and click responses in online marketplaces.
problem Lack of exposure data in recommender systems datasets.
method Proposes a novel dataset including slates and click responses, allowing more accurate likelihood models.
result Models using exposure data show more natural likelihood, reducing bias towards previously exposed items.
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.
In the cost per click (CPC) pricing model, an advertiser pays an ad network only when a user clicks on an ad; in turn, the ad network gives a share of that revenue to the publisher where the ad was impressed. Still, advertisers may be unsatisfied with ad networks charging them for "valueless" clicks, or so-called accid…
Paper addresses bias in search intent affecting click behavior.
problem Bias in user search intent affects click behavior and relevance.
method Proposes a search intent bias hypothesis to improve click models.
result Click models can better interpret user clicks and improve retrieval performance.
New algorithm for maximizing revenue in multinomial logistic bandits.
problem Maximizing revenue in scenarios with multiple outcomes.
method MNL-UCB algorithm based on upper confidence bounds.
result Achieves regret with small dependency on constants.
Online learning to rank is a core problem in information retrieval and machine learning. Many provably efficient algorithms have been recently proposed for this problem in specific click models. The click model is a model of how the user interacts with a list of documents. Though these results are significant, their im…
New model predicts which search snippets will be clicked.
problem Predicting user behavior in search results.
method Proposes a micro-browsing model for analyzing snippet texts.
result Micro-browsing model predicts higher CTR than traditional models.
New algorithm identifies best arm in rare event scenarios.
problem Identifying the best arm with tiny reward probability.
method Approximated Compound Poisson process for faster algorithms.
result Improved computational efficiency with minor sample complexity increase.
A new model improves click-through rate prediction for recommendation systems.
problem Improving accuracy of click-through rate prediction in recommendation systems.
method Combines traditional feature engineering with deep neural networks to automate feature combinations.
result The model (FNFM) outperforms current deep learning feature combination models.
A new model considers fatigue in online content recommendation systems.
problem Fatigue in users due to overexposure and boredom from similar recommendations.
method Proposed a fatigue-aware Dependent Click Model (DCM) and two learning algorithms.
result Developed algorithms with regret bounds for learning content relevance and fatigue effects.
Many web systems rank and present a list of items to users, from recommender systems to search and advertising. An important problem in practice is to evaluate new ranking policies offline and optimize them before they are deployed. We address this problem by proposing evaluation algorithms for estimating the expected …
This paper improves online ad revenue by optimizing auction performance directly.
problem Disconnection between ad ranking and auction performance in online advertising.
method Proposes new loss functions and ranking functions to maximize revenue.
result Proposed methods outperform state-of-the-art in maximizing platform revenue.
Hotel2vec learns hotel embeddings from multiple data sources.
problem Cold-start problem for hotels with insufficient click data.
method Self-supervised neural network architecture combining user clicks, hotel attributes, and geographic info.
result Improved downstream task predictions with structured hotel attributes.
Paper optimizes recommendation systems for long-term business metrics.
problem Short-term reward optimization ignores long-term business metrics.
method Introduced a framework for modeling long-term rewards in RecoGym.
result Proposed a simple extension leading to state-of-the-art results.
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.
Optimizes ranking from click feedback in a bandit setting.
problem Learning to rank from Bernoulli click feedback in a bandit setting.
method Variance-aware confidence sets derived from Bernstein and Chernoff bounds for optimal algorithms.
result Optimal algorithms for the case of small mean rewards, improving on previous suboptimal results.
NuClick uses clicks inside nuclei to improve nuclear segmentation.
problem Lack of efficient tools for nuclear segmentation due to labor-intensive annotation.
method Convolutional neural network framework that uses single point clicks for nuclei segmentation.
result NuClick generates superior segmentation results and facilitates more annotations.
Paper improves recommendation systems by optimizing sequence of items for clicks.
problem Improving recommendation systems robustness against bots and clicks.
method Minimizing pairwise ranking loss over sequences of items, with thresholds to prevent bot influence.
result The proposed algorithms converge and outperform existing methods in various ranking measures.
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Ratestat.ML
Estimating post-click conversion rate (CVR) accurately is crucial for ranking systems in industrial applications such as recommendation and advertising. Conventional CVR modeling applies popular deep learning methods and achieves state-of-the-art performance. However it encounters several task-specific problems in prac…
Improved conversion rate prediction in online advertising using self-supervised pre-training.
problem Data sparsity and calibration issues in predicting conversions given clicks.
method Self-supervised pre-training on all conversion events to enrich CVR prediction model without compromising calibration.
result Improvements in offline training and online A/B tests, with full deployment to Yahoo native advertising system.
App enhances midwives' skills in low-income countries.
problem Low-income countries have high maternal and child mortality rates.
method Deep learning models predict midwives' interest in learning materials.
result Deep learning models accurately predict midwives' interest in learning materials.
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existi…
The goal of online display advertising is to entice users to "convert" (i.e., take a pre-defined action such as making a purchase) after clicking on the ad. An important measure of the value of an ad is the probability of conversion. The focus of this paper is the development of a computationally efficient, accurate, a…
Online learning to rank is a sequential decision-making problem where in each round the learning agent chooses a list of items and receives feedback in the form of clicks from the user. Many sample-efficient algorithms have been proposed for this problem that assume a specific click model connecting rankings and user b…
Unified framework for online learning in click prediction for search ads.
problem Model staleness leading to accuracy and calibration degradation over time.
method Two paradigms of Batch Online Learning: early stopping and proximal regularization.
result Two OL schemes are closely related and can be traded-off between new and historical data.
A rising topic in computational journalism is how to enhance the diversity in news served to subscribers to foster exploration behavior in news reading. Despite the success of preference learning in personalized news recommendation, their over-exploitation causes filter bubble that isolates readers from opposing viewpo…
Simple algorithm approximates rare event frequencies.
problem Approximating the frequency of rare events.
method Iterative update of categorical click-distribution, resulting in a random walk on an n-dimensional simplex.
result The random walk corresponds to a biased Bernoulli convolution under certain conditions.
A new method for faster data annotation using click-supervision and hierarchical object detection.
problem Data annotation bottleneck in modern data collection.
method Semi-automatic approach combining human and neural network for hierarchical object detection.
result Improved annotation speed and accuracy compared to current methods.
New ranking algorithms improve online content delivery by learning from click data.
problem Bias in ranking systems due to production system biases.
method Proposed novel extensions of LinUCB and Linear Thompson Sampling algorithms to handle position-based click model.
result Validated the proposed algorithms through offline and online experiments.
Algorithm learns diverse recommendations from partial clicks.
problem Learning diverse recommendations from partial user feedback.
method Online learning algorithm for cascade model.
result Gap-free upper bound on n-step regret proved.
Proposes a robust learning strategy for RS over implicit feedback.
problem Robustness in training large-scale Recommender Systems over implicit feedback.
method Minimizes pairwise ranking loss over blocks of non-clicked followed by clicked items, discarding updates for abnormal blocks.
result The approach prevents shifts in item distribution due to bots or few user interactions, improving ranking measures and computation time.
Many-to-one RNN predicts user hotel clicks from browsing history.
problem Predicting user hotel clicks from browsing history.
method Combines rule-based algorithm with Gated RNN to sort accommodations.
result Promising results but computationally demanding.
Interprets feature interactions in ad-click prediction models.
problem Improving interpretability of black-box recommender systems.
method Interprets feature interactions from a source model and encodes them in a target model.
result Interpretations significantly outperform existing recommender models.
Proposes DHEB model for predicting online ad performance.
problem Sparse data at individual unit level in online advertising.
method Dynamic Hierarchical Empirical Bayesian (DHEB) model with data-driven hierarchy and shrinkage-based estimations.
result Proposed method outperforms other models in accuracy and efficiency.
Historically, machine learning in computer security has prioritized defense: think intrusion detection systems, malware classification, and botnet traffic identification. Offense can benefit from data just as well. Social networks, with their access to extensive personal data, bot-friendly APIs, colloquial syntax, and …
Comparison Lift uses bandit algorithms to optimize online ad testing.
problem Optimizing online ad testing to maximize click-through rates.
method Bandit-based experimentation algorithm that adapts to test results.
result Ad click-through rates increased by 46% on average.
MCNet: Monotonic Calibration Networks for Expressive Uncertainty Calibration in Online Advertisingcs.LG
MCNet improves uncertainty calibration in online advertising by modeling complex relations and balancing performance.
problem Lack of effective calibration for complex relations and context features in online advertising.
method Introduces MCNet with MCF, order-preserving, and field-balance regularizers.
result Superior performance in generating well-calibrated probability predictions on public and industrial datasets.
Improves CTR prediction by considering spatial and temporal auxiliary ads.
problem Improving CTR prediction in online advertising systems.
method Deep Spatio-Temporal Neural Networks (DSTNs) for CTR prediction.
result DSTNs outperform state-of-the-art methods in CTR prediction.
Model improves CVR estimation in recommender systems by mitigating bias and overlooking causal relationships.
problem Data sparsity and sample selection bias in CVR estimation.
method Entire Space Counterfactual Multitask Model (ESCM) incorporating counterfactual risk minimizer.
result Significantly enhances recommendation performance by effectively mitigating bias and overlooking causal relationships.
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Predictioncs.LG
Proposes a time-aware attention model for CTR prediction.
problem Lack of temporal signals in existing CTR prediction models.
method Time-aware attention model with absolute and relative temporal signals, regularized adversarial sampling.
result Significantly improves CTR prediction performance.