New algorithm improves ad targeting for personalized online services.
problem Personalizing online services for improved user experience and revenue.
method Label ranking approach for non-linear, large-scale prediction of user interests.
result The proposed algorithm outperforms existing solutions in rank loss and top-K retrieval.
Framework for interactive learning to minimize user experience regressions.
problem Sub-optimal user experiences due to frequent exploration of options.
method Explore-Exploit framework for online learning operators.
result Efficiencies achieved in integrating online learning with run-time services.
User releases data to service provider while balancing privacy and utility.
problem Balancing user privacy and service utility in data release.
method Formulated as a Markov decision process (MDP) and solved using deep reinforcement learning (RL).
result Achieved a trade-off between revealing useful information and protecting sensitive data.
SPARKLE handles high-dimensional covariates for online decision-making.
problem Complex reward-covariate relationships in high-dimensional settings.
method SPARKLE uses a sparse additive reward model with doubly penalized estimator and adaptive screening.
result SPARKLE achieves sublinear regret bound logarithmic in covariate dimensionality.
An online learning framework optimizes pricing and capacity in service systems.
problem Optimizing pricing and capacity in dynamic service systems.
method Gradient-based Online Learning in Queue (GOLiQ) framework.
result GOLiQ achieves logarithmic regret bound and improves service provider's performance.
Paper presents a reinforcement learning framework for personalized music playlist generation.
problem Misalignment between offline model objectives and online user satisfaction metrics in conventional playlist recommendation methods.
method Simulation-based reinforcement learning approach using a Deep Q-Network (DQN) modified to address large state and action spaces.
result The modified DQN (AH-DQN) policy leads to better user-satisfaction metrics compared to baseline methods during online A/B tests.
Develops a technique to audit text-generation models trained on personal data.
problem Enforce data-protection regulations like GDPR and detect unauthorized data usage.
method Black-box auditing method that queries a model to detect if a user's data was used for training.
result Successfully audits well-generalized models without overfitting to training data.
NESA learns user preferences and calendar contexts for efficient event scheduling.
problem Challenges in understanding user preferences and complex calendar contexts for automated event scheduling.
method Leverages deep neural networks to learn user preferences and calendar context from raw online calendars.
result Significantly outperforms previous models in personal and multi-attendee event scheduling tasks.
This work addresses privacy issues in IoT data sharing by balancing information disclosure and user privacy.
problem Balancing privacy and utility in time-series data sharing from IoT devices.
method Formulated as POMDPs, solved using A2C DRL, evaluated with synthetic and real data.
result Proposed policies achieve a good balance between privacy and utility.
Privacy-preserving method protects user speech data from cloud services.
problem Privacy compromise in cloud-based speech analysis.
method Collects and sanitizes speech data before sharing, using transformation functions and voice conversion.
result Identification of sensitive emotional state reduced by ~96%.
Policy certifies inventory levels meeting service requirements.
problem Maintaining stock levels meeting service requirements despite unknown demand.
method Data-driven order policy using online learning and integral action.
result Valid inference method for finite samples.
The paper proposes a policy learning framework for interpretable personalization.
problem Effective personalization of goods and services to improve revenues and maintain competitive edge.
method Policy learning with linear decision boundaries using causal inference and Bayesian optimization.
result The learned policy improves net sales revenue by 88.2% and provides insights into important features.
New collaborative algorithm improves personalized mean estimation in online settings.
problem Online estimation of means from multiple, possibly overlapping distributions.
method Novel collaborative strategy for active querying and mean estimation.
result Algorithm improves mean estimates through communication among agents.
POSL is an online learning algorithm for personalized predictions.
problem Real-time personalized predictions for streaming data.
method Online Super Learner algorithm that optimizes predictions with respect to baseline covariates.
result POSL provides reliable predictions and adapts to changing data environments.
New method for efficient personalized learning in mobile health.
problem Efficient and personalized learning in mobile health.
method Proposes a novel generative process on kernel composition for online Gaussian Process regression.
result Trajectories of kernel evolutions can be transferred between users to improve learning and kernels are meaningful for mHealth prediction.
Study shows how online personalization can lead to unfair models due to biased user responses.
problem Fairness issues in online personalization systems due to biased user responses.
method Formulated a regularization-based approach to mitigate biases in machine learning models.
result Demonstrated that online personalization can cause models to learn unfair behavior from biased user responses.
Computational method identifies 30 major complaints in GEICO reviews.
problem Analyzing large numbers of online complaints is challenging.
method Topic modeling approach to reveal latent semantic of complaints.
result 30 major complaints in four categories identified from GEICO reviews.
Improved car-hailing service by analyzing POI effects.
problem Minimize passenger waiting time and optimize vehicle utilization.
method Analyzed POI effects on supply-demand gap and proposed a POI selection scheme integrated with XGBoost.
result More accurate and stable estimation results.
The paper tackles carousel personalization in music streaming apps using contextual bandits.
problem Selecting relevant items to display in carousels for personalized content recommendation.
method Modeling carousel personalization as a contextual multi-armed bandit problem with multiple plays, cascade-based updates and delayed batch feedback.
result Empirically shows the effectiveness of the framework in capturing characteristics of real-world carousels.
The study compares prepaid and postpaid mobile phone users and predicts their subscription type.
problem Predicting mobile phone subscription type based on usage and network connections.
method Graph labelling approach using max-flow min-cut algorithms and indirect inference methods.
result Graph labelling approach achieves 87% classification accuracy, outperforming supervised learning methods.
Study assesses whether RL algorithm personalizes treatment sequences.
problem Evaluate if RL algorithm truly personalizes treatment sequences.
method Resampling-based methodology to investigate personalization.
result RL algorithm's personalization may be due to stochasticity.
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.
Framework optimizes transit routes based on crowd movements using demand prediction and supply optimization.
problem Dynamic optimization of transit routes in areas of crowd movements.
method Combines demand prediction (Quantile Regression) and supply optimization (Linear Programming) to dynamically redesign routes.
result Framework often obtains optimal solutions and outperforms conventional methods.
Dynamic promotion optimization for e-commerce platforms within financial constraints.
problem Balancing promotional costs with incremental revenue for sustainable growth.
method Knapsack Problem formulation for dynamic optimization, Retrospective Estimation, online-dynamic calibration.
result Significant increase in target outcome while staying within financial constraints.
Editorial discusses nine challenges in modern algorithmic trading.
problem Challenges in modern algorithmic trading and controls.
method Discussion of challenges without proposing solutions.
result No specific new results or findings.
A new recommender system learns from user interactions to improve recommendations.
problem Mitigating information overload by personalizing item suggestions.
method Modeling interactions as MDP, using RL to learn optimal strategies, incorporating list-wise recommendations.
result The proposed framework LIRD improves recommendation effectiveness.
Personalized healthcare predictions using deep mixed effect model with Gaussian Processes.
problem Making personalized and reliable predictions from time-series data in healthcare.
method A composite model combining a deep neural network for global trends and Gaussian Processes for individual variability.
result Practical advantages over standard time-series deep models, demonstrated on diverse EHR datasets.
Guarantees for third-person imitation learning from offline data.
problem Improving generalizability in imitation learning.
method Problem-dependent statistical learning guarantees for third-person imitation from offline observation.
result Strong performance guarantees for transferred policies in the offline setting.
Real-time personalization for HAR models learns from new users without prior data.
problem Poor performance of HAR models on new users without labeled data.
method Incremental online domain adaptation using batch normalization.
result Personalized HAR models adapt to new users in real-time.
Two methods estimate effect size for online experiments, improving accuracy and efficiency.
problem Determining the correct effect size for online experiment duration.
method Two approaches: hierarchical models and utility theory.
result Proposed methods outperform baseline approaches in accuracy and efficiency.
Detecting faults and SLA violations in a timely manner is critical for telecom providers, in order to avoid loss in business, revenue and reputation. At the same time predicting SLA violations for user services in telecom environments is difficult, due to time-varying user demands and infrastructure load conditions. In…
Unified neural framework for multi-relational recommender systems.
problem Accurately capturing users' fine-grained preferences from diverse feedback types.
method Multi-Relational Memory Network (MRMN) framework that models fine-grained user-item relations and discriminates between feedback types.
result The proposed MRMN model outperforms state-of-the-art algorithms in various recommender scenarios.
Study finds tax avoidance and IT issues hinder revenue in Gombe state.
problem Problems of personal income tax on revenue generation in Gombe state.
method Survey with primary and secondary data, chi square test.
result Tax avoidance and IT issues are major problems.
A new method for online personalized learning reduces gradient variance by dynamically selecting peers.
problem Online personalized decentralized learning with statistically heterogeneous clients.
method Gradient-based collaboration criterion allowing clients to dynamically select peers with similar gradients.
result The method acts as a variance reduction method, achieving optimal performance in certain conditions.
Maximize revenue by guiding individuals to optimal locations anonymously.
problem Matching supply and demand in online to offline services efficiently.
method Employing maximum entropy principle for independent learning with local aggregated information.
result Significant improvement in joint and individual revenue with fairness.
New AI model optimizes personalized care for elderly residents.
problem Limited care workforce impacts health outcomes and quality of life.
method Bandit algorithms for personalized care planning.
result Improves care quality and health outcomes through personalized care planning.
A new algorithm learns optimal personalized treatment plans online with low regret.
problem Learning optimal dynamic treatment regimes in an online setting.
method Developed a novel algorithm balancing exploration and exploitation for rate-optimal regret.
result Guaranteed rate-optimal regret for linear transition and reward models.
Paper proposes spamGAN to detect and generate opinion spam using limited labeled data.
problem Detecting and preventing opinion spam in online reviews with limited labeled data.
method Generative adversarial network (GAN) trained on semi-supervised data.
result spamGAN outperforms existing techniques in detecting opinion spam with limited labeled data.
Paper proposes machine learning for pricing 3D printing services in marketplaces.
problem Inefficient pricing methods for 3D printing services in marketplaces.
method Data mining and machine learning methods to estimate price ranges based on supplier and customer characteristics.
result Machine learning model achieves 65% accuracy for US suppliers and 59% for Europe suppliers in classifying 3D printer listings.
Paper tackles online learning for DR management with incentives.
problem Estimating baseline consumption in DR programs with consumer incentives.
method Online learning scheme using least-squares with perturbed reward prices.
result Achieves low regret of $\mathcal{O}\left((\log{T})^2
ight)$ compared to optimal.
New framework analyzes LLM personalization trade-offs under congestion.
problem Tension between personalization and resource sharing in LLMs.
method Developed a statistical-economic framework to model user incentives.
result Congestion can flip rankings of SFT and ICL, and offers both methods never hurt profits.
A new thompson sampling method controls for time-varying effects.
problem Dynamic experiments in online services with time-varying effects.
method Odds-ratio Thompson Sampling
result The proposed method works robust to time-varying effects.
Study on electronic banking satisfaction in Nigeria.
problem Limited research on factors enhancing end users' satisfaction in electronic banking.
method Empirical analysis of factors influencing electronic banking user satisfaction.
result Factors influencing electronic banking user satisfaction and their relationship with satisfaction.
Study shows auditing fairness of personalized interventions is impossible due to unknown ground truths.
problem Auditing fairness of personalized interventions in social services, education, and healthcare.
method Point-identification of quantities under monotone treatment response assumption, providing sensitivity analysis for violations.
result Proves impossibility of auditing fairness using standard metrics and provides methods for auditing using partially-identified ROC and xROC curves.
ChOracle predicts user return times to improve churn prediction.
problem Churn prediction in online services.
method Combining Temporal Point Processes and Recurrent Neural Networks with latent variables.
result Superior performance on various real-world datasets.
Paper introduces RTT2Vec for real-time grocery recommendations, achieving 9.4% uplift over baselines.
problem Personalized grocery recommendations to improve user experience and sales.
method RTT2Vec deep architecture for real-time recommendations, approximate inference technique.
result 9.4% uplift in prediction metrics over baseline models.
The paper presents a method for personalized exercise recommendations that improves learner skill gain.
problem Adapting to individual needs in large, diverse groups of learners in digital environments.
method Contextual Thompson Sampling to select exercises that advance learner skill.
result The method recommends exercises associated with greater skill improvement and adapts to learner differences.
SecVM preserves user privacy in training SVMs for classification tasks.
problem Training supervised classifiers on sensitive user data while maintaining privacy.
method A novel secret vector machine (SecVM) framework for training linear SVMs in a distributed, privacy-preserving manner.
result SecVM outperforms baselines in a large-scale online evaluation, preserving user privacy and classification accuracy.