DCM bandits optimize search engine recommendations by learning from user clicks.
problem Optimizing search engine recommendations based on user behavior with multiple clicks.
method Online learning algorithm dcmKL-UCB for maximizing satisfactory item recommendation probability.
result Proves dcmKL-UCB's regret bound and matches a lower bound up to logarithmic factors.
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.
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.
Detects accidental clicks on mobile ads to reduce advertiser costs and improve revenue.
problem Accidental clicks on mobile ads lead to wasted revenue for advertisers and ad networks.
method Collect and analyze dwell time data to identify accidental clicks and estimate thresholds.
result Our method reduces advertiser costs and improves ad click-through rates and revenue.
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.
Efficiently evaluates new ranking policies using click models.
problem Evaluate new ranking policies offline and optimize them before deployment.
method Proposes evaluation algorithms using click models to estimate expected clicks from logged data.
result Our estimators are more statistically efficient than those that do not use click models.
First online learning to rank algorithm for broad click models.
problem Online learning to rank in stochastic click models.
method BatchRank, an algorithm for a broad class of click models.
result Derives a gap-dependent upper bound on the T T T -step regret of BatchRank. 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.
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.
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.
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.
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 explores LETOR for E-Com search, addressing practical challenges and reporting key findings.
problem Applying LETOR to E-Com search presents unique challenges.
method Investigates practical challenges in LETOR for E-Com search, including feature representation, relevance judgments, and feedback signal exploitation.
result LETOR methods can effectively optimize combinations of popularity-based and relevance-based features, and order rate is the most robust training objective.
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.
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.
Proposes a novel network for CTR prediction by learning modality-specific and modality-invariant representations.
problem Learning good representation of items from multimodal features in E-commerce is challenging due to redundant information across modalities.
method Introduces a Multimodal Adversarial Representation Network (MARN) that calculates modality-specific weights and learns modality-invariant representations.
result Consistently achieves remarkable improvements over state-of-the-art methods 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.
TopRank algorithm improves online ranking with better performance and insights.
problem Sequential decision-making in online learning to rank with user feedback.
method Generalized click model and topological sort-based algorithm.
result TopRank outperforms existing algorithms in terms of performance and proof insight.
Generative model learns to trick users into clicking on phishing links.
problem Social engineering and phishing on social media.
method Long Short-Term Memory (LSTM) neural network trained with social media data.
result Achieved state-of-the-art success rates in phishing campaigns.
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.
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.
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.
Paper tackles real-world e-commerce search efficiency and user experience.
problem Efficiently rank large-scale e-commerce search results with multiple factors.
method Design and deploy a novel Cascade ranking model in a large-scale operational e-commerce search application.
result Demonstrates the advantage of the proposed model in addressing multiple factors of effectiveness, efficiency, and user experience.
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…
ESMM models CVR over entire space, overcoming sample selection bias and data sparsity.
problem Sample selection bias and data sparsity in CVR modeling.
method Entire Space Multi-task Model (ESMM) using sequential pattern of user actions.
result ESMM significantly outperforms competitive methods on Taobao dataset.
Proposes a graph-based system for personalized news recommendation considering multiple user behaviors.
problem Lack of considering multiple user behaviors in news recommendation systems.
method Builds an interaction behavior graph, applies DeepWalk and G-CNN for news and behavior sequence representations, introduces core and coritivity features.
result Achieves personalized news recommendation considering user's concentration degree of interests.
Proposes a new algorithm for click feedback in search results.
problem Learning to predict user clicks based on relevance and position.
method Developed a Bernoulli rank- 1 1 1 bandit learning problem and proposed Rank1ElimKL to improve performance. result Rank1ElimKL outperforms Rank1Elim in various scenarios, including real-world data.
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.
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 i l d e O ( d K T ) ilde{\mathcal{O}}(dK\sqrt{T}) i l d e O ( d K T ) with small dependency on constants. RippleNet uses a knowledge graph to improve recommendation by propagating user preferences.
problem Collaborative filtering sparsity and cold start problem.
method End-to-end framework that propagates user preferences over the knowledge graph.
result Ripple Network achieves substantial gains in recommendation performance.
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.
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.
Optimized OCPC strategy improves Taobao's ad traffic allocation efficiency.
problem Inefficient bid matching between advertisers and traffic quality.
method Proposed OCPC strategy to automatically adjust bids for finer matching.
result Substantially better results compared to fixed bid methods in production tests.
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.
DCN combines DNN and cross network for efficient ad click prediction.
problem Efficiently learn feature interactions in prediction models.
method Deep & Cross Network (DCN) that explicitly applies feature crossing at each layer.
result Demonstrated superior performance in model accuracy and memory usage.
UCB algorithms estimate uplifts in multi-variable reward systems.
problem Marketing campaigns and recommender systems with multiple outcome variables.
method UCB-style algorithms for estimating uplifts over a baseline.
result Sublinear regret bounds for multiple problem variants.
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.
A new framework for personalized ad retrieval in e-commerce search.
problem Difficulty in measuring ads retrieved using multiple signals (e.g. user profiles, clicks).
method Employing historical click data to initialize a hierarchical network representing signals, keys, and ads. Training a model to learn weights of edges and selecting the best edges.
result Framework achieves good performance, improving RPM/CTR.
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.
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 2 ^2 2 ) incorporating counterfactual risk minimizer. result Significantly enhances recommendation performance by effectively mitigating bias and overlooking causal relationships.
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.
Proposes a new model to predict click-through rates by learning user interests.
problem Difficulty in capturing diverse user interests from historical behaviors.
method Introduces a local activation unit to adaptively learn user interests from historical behaviors for each ad.
result Improves model's expressive ability by varying representation vectors over different ads.
DKN uses knowledge graphs to improve news recommendation.
problem Limited personalized news recommendations due to lack of external knowledge.
method Integrates knowledge graph representation into news recommendation using a deep knowledge-aware network (DKN).
result DKN achieves substantial gains over state-of-the-art models in click-through rate prediction.