Develops a real-time exercise recommendation system using deep learning.
problem Improving accuracy in exercise recommendation systems without user feedback.
method Deep recurrent neural network with attention mechanisms, real-time expert feedback.
result Improved accuracy in exercise recommendation system after real-time active learning.
Paper proposes MACDAE to infer real-time O2O contexts.
problem Difficult to infer users' real-time contexts, especially implicit ones, for O2O recommendation.
method MACDAE: a model that infers implicit contexts from user-item-explicit context interactions.
result Significant improvements in click-through rate and conversion rate in real-world traffic.
This paper proposes a real-time signal plan recommendation system for traffic incidents.
problem Limited effectiveness of traffic incident management due to late response and workload.
method Decomposes recommendation task into real-time traffic prediction and plan association, learning from historical data and metric learning.
result Precision score of 96.75% and recall of 87.5% on testing plan, with 22.5 minutes lead time ahead of Waze alerts.
RALM extends exposure for long-tail contents in real-time recommender systems.
problem Lack of timely exposure for long-tail contents in recommender systems.
method Real-time attention-based look-alike model (RALM) with seeds-to-user similarity prediction and user representation learning.
result RALM outperforms traditional look-alike models in effectiveness and real-time performance.
iPrescribe offers fast online offer recommendations using deep learning.
problem Online offer recommendation in real-time.
method Ensemble of deep learning and machine learning algorithms, optimized streaming technology stack, and efficient LSTM deployment.
result 90th percentile recommendation latency of 38 milliseconds.
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.
New cold-start recommendation method for recommender systems.
problem Making accurate recommendations for users and items with limited data.
method Proposes a faster cold-start prediction formula in collective matrix factorization models.
result Improved cold-start recommendations with reliable predictions for new users, better than non-personalized recommendations.
System learns user preferences to synthesize materials quickly.
problem Slow material synthesis for novice and expert users.
method Gaussian Process Regression for user preferences, neural network for real-time image predictions.
result Real-time material synthesis enables novice users to generate hundreds of models.
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…
New system assigns vehicles to routes for cost and energy efficiency.
problem Optimizing vehicle route assignment for cost and energy efficiency.
method Machine learning-based neural network algorithm to estimate energy consumption and provide real-time recommendations.
result Demonstrated efficient vehicle assignment for medium and heavy duty trucks.
Paper proposes real-time risk metrics for stablecoin protocols.
problem Lack of risk management frameworks for stablecoins.
method Developed two risk metrics: capitalization and liquidity.
result Demonstrated practical benefits of real-time on-chain data.
Distributed Collaborative Hashing improves recommendation efficiency in big data.
problem Efficiency in offline model training and online recommendation for collaborative filtering.
method Distributed Learning Framework + Hashing Technique.
result DCH model achieves comparable recommendation accuracy with fast convergence and real-time efficiency.
SAFER improves personalized treatment recommendations for dynamic clinical contexts.
problem Personalized treatment optimization in evolving clinical contexts with safety concerns.
method Integrates structured EHR and clinical notes, uses conformal prediction for safe recommendations.
result SAFER outperforms state-of-the-art baselines in recommendation metrics and mortality rates.
PoDiRe learns long-term rewards in multi-task recommendations.
problem Long-term rewards in multiple recommendation tasks.
method Policy Distilled Reinforcement Learning (PoDiRe) combining deep reinforcement learning and knowledge distillation.
result PoDiRe outperforms state-of-the-art methods in real-world data.
DVAO predicts volumetric ambient occlusion for real-time volume rendering.
problem Predicting per-voxel ambient occlusion in volumetric data sets.
method Deep learning neural network that considers global information through transfer function.
result DVAO supports real-time volume interaction and generalizes to various modalities.
Unified approach for conversational recommendation by integrating attributes and items.
problem Cold-start users' real-time personalization in online recommendation.
method Seamlessly unifies attributes and items in Thompson Sampling framework for interactive decision-making.
result Conversational Thompson Sampling (ConTS) outperforms existing methods in success rate and conversation turns.
Paper introduces online tensor inference for real-time data analysis.
problem Real-time processing of high-dimensional tensor data.
method Stochastic Gradient Descent (SGD) for efficient online inference.
result Establishes non-asymptotic convergence and optimal estimation error rate.
This work recommends personalized search stories to users based on their interests.
problem Personalized search story recommendation within search engines.
method Deep reinforcement learning architecture trained by imitation learning and reinforcement learning.
result Empirically demonstrated effectiveness on real-world data sets.
This paper improves recommender systems by handling dynamic user preferences and item popularity.
problem Dynamic user preferences and changing item popularity in recommender systems.
method Developed a Thompson sampling-based policy for a high-dimensional linear bandit problem, reducing feature vector dimensionality and using exponentially increasing weights.
result Proved a regret bound that scales with the reduced dimension, demonstrating effectiveness in trade-off between computational complexity and regret performance.
New framework reduces factorisation model run time by exploiting sparse vector geometry.
problem Inefficient inner product computations over large sparse vectors in real-time applications.
method Geometry-aware permutation maps on a tessellated unit sphere for sparse vector embeddings.
result Significant reduction in run time with minimal accuracy loss.
Bayesian algorithm learns consumer preferences for energy-saving home automation.
problem Effective energy saving for residential consumers in real-time tariffs.
method Bayesian learning algorithm to estimate comfort level from appliance use history.
result Algorithm outperforms regression analysis in numeric experiments with simulated consumer behavior.
New methods for faster ranking and link prediction using higher-order motifs.
problem Real-time ranking and link prediction in applications like web search.
method Higher-order ranking and link prediction methods based on closing higher-order network motifs.
result The methods are faster and more efficient than existing methods based on closing triangles.
A new method predicts student skill success rates in real-time.
problem Accurate and explainable prediction of student skill success rates in real-time.
method Performance Distribution Tracing (PDT) using a Dynamic Bayesian Network with continuous random variables.
result PDT provides both explainability and accuracy in real-time predictions of student skill success rates.
Neural pedagogical agent updates user models in real-time for mobile education apps.
problem Real-time user modeling for dynamic mobile education platforms.
method Bidirectional recurrent neural networks with attention mechanism over embedded question-response pairs.
result Model outperforms existing approaches in predicting user response correctness.
Machine learning improves prediction of complex geology ahead of drilling.
problem Predicting complex geology during drilling in real-time.
method Generative Adversarial Network (GAN) and Forward Deep Neural Network (FDNN) for real-time geological uncertainty reduction.
result Real-time estimates of complex geological uncertainty achieved.
LLMs show biases in investment analysis, leading to unreliable recommendations.
problem LLMs face conflicts between pre-trained knowledge and real-time market data, leading to biases in investment analysis.
method Experimental framework to investigate emergent behaviors in LLMs, analyzing sector, size, and momentum biases.
result Distinct, model-specific biases observed, including a tendency to prefer technology stocks, large-cap stocks, and contrarian strategies.
DINOSAUR improves retrieval by accounting for embedding uncertainty in recommender systems.
problem Retrieval bias towards popular items due to noisy embeddings.
method Samples multiple embeddings per item and queries with sampled embeddings to account for uncertainty.
result Improves coverage of long-tail niche content without sacrificing recall.
Proposes a deep RL framework for optimizing task allocation in crowdsourcing platforms.
problem Optimizing task allocation in crowdsourcing platforms for both workers and requesters.
method Uses Deep Q-Network (DQN) to estimate long-term rewards, considering immediate and future rewards simultaneously.
result Demonstrates superior performance in synthetic and real datasets.
Paper tackles dynamic assortment with dual contexts, improving revenue in e-commerce.
problem Maximizing revenue in e-commerce with personalized recommendations from vast catalogs.
method Low-rank dynamic assortment model and upper confidence bound approach.
result Regret bound of i l d e O ( ( d 1 + d 2 ) r T ) ilde{O}((d_1+d_2)r\sqrt{T}) i l d e O (( d 1 + d 2 ) r T ) for dynamic assortment problem. The study examines how alternative resource adequacy contract designs affect market participants' risk profiles and resource mix.
problem The tension between promoting reliability and competition in liberalized electricity markets.
method Constructs a stochastic equilibrium model of a competitive market with incomplete risk trading and computes investment equilibria under different contracting regimes.
result Alternative contracting regimes can induce different risk profiles and resource mixes, affecting market outcomes.
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.
RL platform enhances user journeys in healthcare apps.
problem Improving user experience and personalization in healthcare apps.
method Reinforcement learning framework for adaptive interventions.
result Significant increase in basket size through personalized recommendations.
New RL framework improves real-time control performance.
problem Real-time RL systems assume static states, leading to suboptimal outcomes.
method Introduces a new real-time RL framework where states and actions evolve simultaneously.
result RTAC algorithm outperforms existing state-of-the-art algorithms in real-time and non-real-time settings.
New deep learning method for real-time regression analysis.
problem Real-time regression analysis for time series data.
method Novel deep learning algorithms for real-time regression analysis.
result Demonstrated real-time regression analysis for time series data.
This study compares two neural models for financial forecasting, showing their superiority.
problem Improving financial market trend predictions using neural networks.
method Systematic comparison of N-HiTS and N-BEATS with conventional models.
result N-HiTS and N-BEATS enhance forecast accuracy and robustness in financial time series data.
Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.
problem Recommender systems learn from user choices but can stall if users blindly follow recommendations.
method The recommender learns user knowledge by observing choices, abstaining from recommending a choice when multiple alternatives produce similar payoffs.
result Learning rate and social welfare improve when the recommender abstains from recommending certain choices.
PRISM provides real-time SLAM with uncertainty estimates for agent and map states.
problem Lack of uncertainty estimates and real-time capability in SLAM.
method Combines differentiable rendering and 6-DoF dynamics, uses approximations for Bayesian inference.
result Runs at 10Hz real-time with similar accuracy to state-of-the-art SLAM.
We create real-time geodesic rendering for non-isotropic geometries.
problem Challenging visualization of non-isotropic geometries.
method Novel methods for real-time native geodesic rendering.
result Methods can be applied to visualization, machine learning, and video games.
CAFL breaks feedback loops in recommender systems using causal inference.
problem Feedback loops in recommender systems compromise recommendation quality and homogenize user behavior.
method Causal Adjustment for Feedback Loops (CAFL) algorithm that breaks feedback loops using causal inference.
result CAFL improves recommendation quality compared to prior correction methods.
Improved real-time UAV terrain following with RVM-RLS filter.
problem Accurate real-time waypoints estimation under measurement noise in nonlinear, time-varying systems.
method Residual Variance Matching Recursive Least Squares (RVM-RLS) filter guided by RVME criterion.
result Improved waypoints estimation accuracy by approximately 88% compared to benchmarks.
Proposes a new model for diversified interactive recommendation.
problem Previous methods focus on optimizing recommendation accuracy, ignoring diversity.
method DC 2 ^2 2 B model using determinantal point process and Thompson sampling-based variational Bayesian inference. result Demonstrates effectiveness of the proposed method through extensive experiments.
FedCM measures contributions in real-time for federated learning.
problem Fairly allocating contributions in federated learning systems.
method FedCM calculates impact based on current and previous rounds with attention aggregation.
result FedCM is more sensitive to data quality and quantity in real-time.
Guidelines for deploying deep learning models on smartphones are developed.
problem Lack of unified guidelines for real-time deployment of deep learning solutions on smartphones.
method Unified flow of implementation for Android and iOS, use of multi-threading, benchmarking framework.
result Developed deployment approach allows easy conversion of deep learning models into real-time smartphone apps.
Poisoning attacks improve graph-based recommender system recommendations.
problem Designing effective poisoning attacks for graph-based recommender systems.
method Formulated as an optimization problem, solved with techniques to assign rating scores to fake users.
result Outperforms existing attacks for graph-based recommender systems, recommending target items to 580 times more normal users.
Paper introduces a new job recommendation method using candidate job selection progression.
problem Traditional job recommendation methods are either filter-based or feature-based, limiting serendipitous and cold-start recommendations.
method Uses machine learning to analyze candidate job selection progression and derive latent competencies.
result Achieved best click-through rate in a real-world job recommender system.
Machine learning biases in job recommendations can lead to unfair outcomes.
problem Biased recommendations from recommender systems in job matching.
method Addressing biases at various stages of recommender systems training and deployment.
result Techniques can reduce bias in job recommendations, ensuring fair outcomes.
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.
This paper develops a neural network method for real-time feedback in simulation-based training.
problem Real-time feedback for novice skill improvement in simulation-based training.
method Adversarial neural network technique to minimize L1 regularized loss.
result The proposed method generates effective and efficient real-time feedback.