New framework optimizes time use considering health uncertainty.
problem Uncertainty in data-driven time-use predictions leads to unreliable recommendations.
method Quality Diversity (QD) framework incorporating predictive uncertainty.
result More reliable time-use recommendations balancing expected benefits and model confidence.
New algorithm for online collaborative filtering using linear bandits and alternating least squares.
problem Online collaborative filtering with item recommendations over time.
method Combines linear bandits and alternating least squares for matrix factorization.
result Superior performance in cumulative regret and average cumulative NDCG over state-of-the-art algorithms.
Boosting framework improves Factorization Machines for recommendation systems.
problem Finding informative interactions between features in recommendation systems.
method Adaptive Boosting framework of Factorization Machines (AdaFM) that adaptively searches for proper ranks.
result AdaFM outperforms state-of-the-art Factorization Machines on real-world datasets.
Wearable tech detects table tennis shots with high accuracy.
problem Lack of shot detection in table tennis using wearables.
method Fusion of IMU and audio sensor data for real-time shot detection.
result 95.6% accuracy in shot detection.
Graph DNA uses Bloom filters to efficiently encode deep graph neighborhoods for better collaborative filtering.
problem Collaborative filtering struggles with exploiting deeper graph neighborhoods due to high time and space complexity.
method Graph DNA employs Bloom filters to compute approximate deep neighborhood information in linear time, enabling efficient encoding and utilization in collaborative filtering.
result Graph DNA significantly improves collaborative filtering performance with minimal computational and memory overhead.
We provide a complete characterization of the class of one-dimensional time-homogeneous diffusions consistent with a given law at an exponentially distributed time using classical results in diffusion theory. To illustrate we characterize the class of diffusions with the same distribution as Brownian motion at an expon…
We adress the maximization problem of expected utility from terminal wealth. The special feature of this paper is that we consider a financial market where the price process of risky assets can have a default time. Using dynamic programming, we characterize the value function with a backward stochastic differential equ…
Proves a Minkowski inequality for static Einstein-Maxwell space-time.
problem Understanding the photon sphere in static Einstein-Maxwell space-time.
method Inverse mean curvature flow (IMCF) approach.
result Proves a Minkowski-like inequality for asymptotically flat static Einstein-Maxwell space-time.
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.
Deep learning detects traffic accidents in real time using spatiotemporal data.
problem Detecting traffic accidents to improve safety and reduce delays.
method Used LSTM and GRUs on spatiotemporal sequential data with SMOTE oversampling.
result GRU model performs slightly better than LSTM in detecting traffic accidents.
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.
Proposes a new model for diversified interactive recommendation.
problem Previous methods focus on optimizing recommendation accuracy, ignoring diversity.
method DC2B model using determinantal point process and Thompson sampling-based variational Bayesian inference. result Demonstrates effectiveness of the proposed method through extensive experiments.
The paper analyzes risk assessment for cash flows in continuous time using the notion of convex risk measures for processes. By combining a decomposition result for optional measures, and a dual representation of a convex risk measure for bounded \cd processes, we show that this framework provides a systematic approach…
System detects multiple patients' behaviors in real-time using mmWave radar and CNN.
problem Real-time patient behavior monitoring in hospitals.
method Used mmWave radar for tracking and collecting Doppler patterns. Created a three-layer CNN model for behavior classification.
result System achieved very good inference accuracy in predicting patient behaviors in real-time.
Inverse mean curvature flow converges to a disk in hyperbolic space.
problem Understanding flow behavior in hyperbolic geometry.
method Inverse mean curvature flow with free boundary on geodesic spheres.
result Flow converges to a totally geodesic disk.
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.
Paper proposes a hybrid system to address bias in recommender systems.
problem Bias in recommender systems exacerbates existing societal inequalities.
method Hybrid approach combining multiple similarity measures, content, and demographic info.
result Our model provides more accurate and fairer recommendations.
Extends recommender methods to respect capacity constraints.
problem Recommendation under capacity constraints in various settings.
method Extend three state-of-the-art latent factor recommendation approaches (PMF, GeoMF, BPR) to optimize for both recommendation accuracy and expected item usage that respects capacity constraints.
result Experimental results highlight the benefit of the method for recommendation under capacity constraints.
DeepFair improves fairness in recommender systems without sacrificing accuracy.
problem Lack of bias management in recommender systems leads to unfair recommendations for minority groups.
method Deep Learning based Collaborative Filtering algorithm that balances fairness and accuracy.
result It is possible to make fair recommendations without losing significant accuracy.
The paper aims to define a benchmark for deep learning recommendation models.
problem Insufficient benchmarking for deep learning recommendation models.
method Synthesizes modeling strategies, defines desirable characteristics, and summarizes advice from the MLPerf Recommendation Advisory Board.
result Defines an industry-relevant benchmark for deep learning recommendation models.
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.
New algorithm uses negative user preferences to improve recommendation quality.
problem Insufficient utilization of negative user preferences in recommender systems.
method Explicitly models negative user preferences to recommend more positive items.
result Improves recommendation quality by increasing accuracy and reducing negative items.
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.
Survey on using knowledge graphs for better recommender systems.
problem Data sparsity and cold start issues in recommender systems.
method Utilizes knowledge graphs to improve recommendation accuracy and provide explanations.
result Advantages of knowledge graph-based recommender systems.
Advances citation and subject label recommendation using multi-modal adversarial autoencoders.
problem Improving recommendation systems for citations and subject labels.
method Multi-modal adversarial autoencoders with adversarial regularization, sparsity, and input modality analysis.
result Adversarial regularization consistently improves recommendation performance.
Data poisoning attacks can manipulate recommender systems to recommend target items.
problem Attacks on recommender systems to influence top-N item recommendations.
method Formulated as an optimization problem, solved using influence function to select influential users.
result Effective data poisoning attacks that outperform existing methods.
Proposes CF-SFL to improve sparse data recommendation.
problem Poor performance of CF in sparse data.
method Generative user feedback loop to simulate user feedback.
result Improves recommendation results on multiple datasets.
The paper improves recommendation models by considering user interactions with recommended items.
problem Improving next item prediction in recommendation systems.
method Extending RNN framework with a recommendation action module and state-action fusion module.
result Improved performance on next item prediction compared to baselines.
Deep RL system learns from user feedback to improve recommendations.
problem Challenges in incorporating negative feedback into recommender systems.
method Modeling interactions as MDP, using RL to learn optimal strategies, incorporating both positive and negative feedback.
result Deep recommender system (DEERS) effectively learns from user feedback.
A new method for better recommendation by accounting for hidden factors.
problem Hidden factors affecting both watched movies and ratings.
method Two-stage probabilistic models to remove bias due to confounding.
result Improved recommendation and stable performance against interventions.
A hybrid approach uses RNNs to recommend news articles based on context and session history.
problem Challenging news recommendation due to varying user interests and factors.
method Context-aware, hybrid, deep learning approach using RNNs with additional information types.
result Significantly higher recommendation accuracy and catalog coverage compared to other session-based algorithms.
Improved Top-N recommendations with novel rank approximation.
problem Low accuracy in recommender systems.
method Linear sparse and low-rank representation with nuclear norm relaxation.
result Significantly improved Top-N recommendation accuracy.
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.
Survey of IoT recommendation systems and their limitations.
problem Traditional recommender systems fail to handle IoT data.
method Comprehensive review of IoT recommender systems and techniques.
result Proposes a reference framework for future research.
Scalable hyperbolic recommender system outperforms Euclidean models on complex networks.
problem Complex network datasets require more efficient recommendation models.
method Used hyperbolic geometry, novel hyperbolic model, and Einstein midpoint for asymmetric recommendations.
result Hyperbolic recommender systems significantly outperform Euclidean models on complex network datasets.
ComiRec framework predicts user interests for personalized recommendations.
problem Predicting user interests from sequential behavior data.
method ComiRec framework captures multiple user interests and balances recommendation accuracy and diversity.
result ComiRec achieves significant improvements over state-of-the-art models in sequential recommendation.
BoostJet combines statistical aggregates and neural embeddings for better recommendations.
problem Combining diverse user and offer features for improved recommendation quality.
method Integrates statistical aggregates and neural embeddings using MatrixNet.
result Significantly improved recommendation quality on Yandex's dataset.
Intelligent recommender system tracks user activity and intent for better recommendations.
problem Recommender systems often lack user intent awareness, leading to suboptimal recommendations.
method Encoded user activity, reduced to lower dimensions using tensor factorization, and scored for intent. Combined with contextual information for ranking recommendations.
result Better recommendations compared to baselines, with intent-aware scoring.
A study compares local music recommendation algorithms, finding neighborhood-based methods perform best.
problem Cold-start problem for local artists with little user preference data.
method Comparison of three recommender system algorithms (IIN, ALS, BPR) on local music recommendation task.
result Neighborhood-based approach (IIN) performs best for local music recommendation.
Unified deep framework for personalized recommendations with uncertainty.
problem Uncertainty in user preferences in recommendation systems.
method Gaussian embeddings, Monte-Carlo sampling, convolutional neural networks.
result Superior performance in recommendation accuracy compared to state-of-the-art models.
Deep neural networks improve recommendation accuracy in marketplaces.
problem Measuring and optimizing recommender performance in marketplaces.
method Hybrid item representation models, sequence-based models, and multi-armed bandit models.
result Promising deep neural network recommenders are currently in production at FINN.no.
Robots adaptively monitor changing phenomena with MCMC and GM.
problem Persistent monitoring of changing real-world phenomena with limited resources.
method Adaptive Gaussian Mixture model for covariance learning, Sequential Monte Carlo for adaptation, greedy entropy maximization.
result Robots optimize informative regions locally and adaptively.
Proposes a deep hybrid model for better recommendation systems.
problem Limited studies on hybrid recommender systems and the need for more advanced approaches.
method Integrates deep learning with ID embeddings and auxiliary features for improved recommendation.
result Improves recommendation results over deep learning models using ID embeddings.
SharedMF uses secret sharing to protect privacy in distributed recommendation systems.
problem Privacy issues in multi-source data for recommendation systems.
method Federated learning and secret sharing technology.
result SharedMF achieves faster execution speed and better data adaptability compared to homomorphic encryption methods.
Tensor methods enhance recommender systems with context-aware recommendations.
problem Traditional recommender systems are limited in their ability to provide context-aware recommendations.
method Tensor factorization techniques are applied to improve recommendation accuracy and contextuality.
result Tensor-based recommender systems produce more accurate, context-aware recommendations.