Personalized activity recognition improves performance for diverse users.
problem Poor performance of impersonal algorithms for individual users.
method Personalized activity recognition using deep embeddings from a fully convolutional neural network with triplet loss.
result Novel subject triplet loss provides the best performance overall.
A scalable framework preserves personalized higher-order network proximities.
problem Lack of expressive methods to preserve personalized higher-order network proximities.
method Incorporates random walk into a sound objective to preserve arbitrary higher-order proximities and introduces random walk with restart for personalized-weighted preservation.
result Consistently and substantially outperforms state-of-the-art methods on real-world networks.
FURL improves model accuracy in FL by locally training user embeddings.
problem Improving prediction accuracy of neural-network-based models in Federated Learning.
method FURL divides model parameters into federated and private parameters, training private parameters locally.
result Significant performance improvement with 8% and 51% increases on two datasets.
PinnerSage creates multi-modal user embeddings for better Pinterest recommendations.
problem Single high-dimensional embeddings fail to fully understand user interests.
method Hierarchical clustering and Medoids to summarize user actions into coherent clusters.
result PinnerSage outperforms single embedding methods in personalized recommendations.
SPIRE enables efficient federated learning for diffusion models by separating client-specific embeddings from a shared backbone.
problem Large diffusion models are impractical for federated learning due to their size.
method SPIRE separates the network into a global backbone and client-specific embeddings, enabling efficient finetuning.
result SPIRE achieves parameter-efficient finetuning, updating only a small fraction of weights.
Unified product embeddings improve cross-task performance in e-commerce.
problem Training product embeddings in isolation limits cross-task performance.
method Combining text, clickstream, and image data using denoising auto-encoders, BPR, and Siamese neural networks.
result Unified product embeddings uniformly outperform isolated embeddings across three e-commerce tasks.
ConCare personalizes healthcare predictions by capturing EMR features.
problem Predicting patient outcomes from EMR data with personalization.
method Captures personal characteristics and time-aware distribution in EMR data.
result Improves healthcare prediction accuracy through personalized health context.
Author2Vec generates user embeddings from social media data.
problem Generating useful user embeddings from noisy social media data.
method End-to-end neural network with BERT sentence representations and unsupervised pre-training.
result Author2Vec outperforms traditional methods in user classification tasks.
Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applicatio…
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.
A new method for embedding heterogeneous networks using spacey random walks.
problem Stationarity issues in meta-path guided random walks for HIN embedding.
method Heterogeneous personalized spacey random walk.
result Substantial improvement over existing network embedding algorithms.
Improved music recommendations using hyperbolic embeddings and Bayesian estimation.
problem Generating personalized music recommendations.
method Hyperbolic Poincaré embeddings for hierarchies, empirical Bayes for link reliability.
result Significant performance improvement in A/B testing.
Personalized Transformer improves temporal collaborative ranking performance.
problem Temporal collaborative ranking in recommendation systems.
method Personalized Transformer model using attention mechanisms.
result Personalized Transformer outperforms SASRec by almost 5% in NDCG@10.
Differentially private graph learning via bounded sensitivity PPR.
problem Protecting user data in graph learning algorithms.
method Proposes a sensitivity-bounded personalized PageRank (PPR) algorithm.
result Achieves similar accuracy to non-private algorithms with large degrees.
Personalized federated learning improves model accuracy and fairness by leveraging shared representations and local memorization.
problem Sub-optimal performance of federated learning when client data distributions are heterogeneous.
method Proposes a personalization mechanism based on local memorization of shared representations from a collectively trained global model.
result Significantly higher accuracy and fairness compared to state-of-the-art methods.
Paper tackles variable-length, incomplete wearable sensor data to improve personalized insights.
problem Variable-length and incomplete time series data from wearable sensors.
method HeartSpace integrates a time series encoding module and pattern aggregation network, along with a Siamese-triplet network for representation learning.
result Empirical evaluation shows significant performance gains in personality prediction, demographics inference, and user identification.
SessionPath improves category suggestions in type-ahead search.
problem Improving precision and recall in eCommerce type-ahead suggestions.
method SessionPath uses session embeddings and a probability distribution model to predict facets.
result SessionPath outperforms count-based and neural models in eCommerce shops.
A new system combines vision and language for person re-identification.
problem Real-world surveillance lacks visual data for person re-identification.
method Two-stream CNN framework with shared logits, CCA for modalities, multi-modal testing protocol.
result 22% improvement in re-identification performance with multi-modal queries.
A new method models user-specific parameters as a low-rank plus sparse component for efficient personalization.
problem Efficient personalization of machine learning models for individual users.
method Meta-learning approach that models network weights as a sum of low-rank and sparse components.
result The proposed method, AMHT-LRS, achieves nearly optimal sample complexity for estimating the low-rank and sparse components.
Personal VAD detects target speaker voice activity efficiently.
problem Efficiently detect target speaker voice activity for reduced computational cost and battery usage.
method Trains a neural network conditioned on speaker embedding or verification score, outputs probabilities for three speech classes.
result Trained model with 130K parameters outperforms combined standard VAD and speaker recognition networks.
We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed. We expect that these embeddings will be useful for the construction of predictive statistical models…
Two algorithms improve Federated RL in diverse environments.
problem Collaborative learning in environments with varying dynamics.
method Proposed two federated RL algorithms, QAvg and PAvg, and a personalization heuristic.
result Achieved better performance and generalization in diverse environments.
Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) --- the most widely used model in recommendation --- as a demonstration, we show that optimizing it…
Deep learning predicts fit for fashion e-commerce.
problem Predicting correct fit for customer satisfaction and cost reduction.
method Deep learning content-collaborative approach using customer and article embeddings.
result Significant improvement over state-of-the-art methods.
Deep neural network predicts product returns before purchase.
problem High costs of handling returned fashion products.
method Bayesian Personalized Ranking (BPR) embeddings and skip-gram model for user and product features.
result Reduced overall returns through real-time return probability prediction.
We introduce a nonlinear method for directly embedding large, sparse, stochastic graphs into low-dimensional spaces, without requiring vertex features to reside in, or be transformed into, a metric space. Graph data and models are prevalent in real-world applications. Direct graph embedding is fundamental to many graph…
MPVAA learns holistic patient representations from mixed healthcare data.
problem Learning personalized patient representations from heterogeneous healthcare data.
method Mixed Pooling Multi-View Attention Autoencoder (MPVAA) that integrates non-linear relationships among multiple data modalities.
result MPVAA generates more effective patient representations than state-of-the-art methods.
A model for POI recommendation using relation embedding.
problem Challenges in POI recommendation due to sparse user-POI matrix and varying context.
method Translation-based relation embedding using Knowledge Graph Embedding techniques, combined matrix factorization framework.
result Demonstrates effectiveness of the proposed model on real-world datasets.
In today's day and age when almost every industry has an online presence with users interacting in online marketplaces, personalized recommendations have become quite important. Traditionally, the problem of collaborative filtering has been tackled using Matrix Factorization which is linear in nature. We extend the wor…
The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task. Such systems must make sense of enormous amounts of implicit feedback in order to understand user preferences across numerous product categories. This paper presents a deep learning based solution t…
XploVAE improves recommendation by balancing known and novel items.
problem Balancing known and novel items for better recommendations.
method Constructs user-specific subgraphs for exploitation and exploration, learns personalized item embeddings.
result Demonstrates effectiveness on various real-world datasets.
HGP tackles noisy connections and heterogeneity in social graphs for better social recommendation.
problem Challenges in social graphs, especially noisy connections and heterogeneity, hinder GNN-based social recommendation.
method HGP uses a tripartite graph to reduce complexity, personalized PageRank for node embeddings, and attention mechanism for integration.
result HGP outperforms baselines in AUC and F1-score metrics on a large-scale dataset.
Personalized stress model using transfer learning from 20 participants.
problem Limited generalizability of machine learning models due to individual physiological differences.
method Transfer learning from a base model trained on 20 participants' physiological data collected in real-time.
result Improved model personalization and cross-domain performance.
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints. Due to the large-scale and dynamic nature of resource coordination in hospitals and factories, human domain experts manually plan a…
For assistive robots and virtual agents to achieve ubiquity, machines will need to anticipate the needs of their human counterparts. The field of Learning from Demonstration (LfD) has sought to enable machines to infer predictive models of human behavior for autonomous robot control. However, humans exhibit heterogenei…
This work analyzes PPR-based node embeddings and their topological information.
problem Understanding and interpreting PPR-based node embeddings.
method Unified framework and two methods for topology recovery.
result PPR-based embeddings maintain more topological information than random walk-based embeddings.
The paper explores how machine learning personalization aligns with humanistic views of the person.
problem Aligning machine learning personalization with humanistic views of the person.
method Explicating the concept of personalization and contrasting it with humanistic views of the person.
result Proposes dimensions for evaluating the degree of personalization of ML personalized scores.
Network embedding converts network data into vectors for machine learning.
problem Machine learning tasks on networks require vector representations of nodes and links.
method Various unsupervised and supervised methods for converting network data into vectors.
result Network embedding methods aim to preserve network structure in learned feature representations.
Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled…
The widespread availability of electronic health records (EHRs) promises to usher in the era of personalized medicine. However, the problem of extracting useful clinical representations from longitudinal EHR data remains challenging. In this paper, we explore deep neural network models with learned medical feature embe…
This paper debiases KG embeddings by filtering sensitive attributes.
problem Social and cultural biases in KG representations.
method Exploring and filtering popularity and gender biases in KGE.
result Sensitivity of KG embeddings to sensitive attributes like gender.
Due to physiological variation, patients diagnosed with the same condition may exhibit divergent, but related, responses to the same treatments. Hidden Parameter Markov Decision Processes (HiP-MDPs) tackle this transfer-learning problem by embedding these tasks into a low-dimensional space. However, the original formul…
Financial institutions use LSTM models to predict customer goals.
problem Predicting customer goals and actions in financial services.
method Used LSTM models with state-space graph embeddings on historical customer traces.
result Demonstrated the effectiveness of LSTM models in predicting customer goals and actions.
Study predicts social relationships using triadic influence from social networks.
problem Difficulty in quantifying social relationships and their dynamics.
method Real social networks of 13 schools, neural networks, high-dimensional embedding.
result Triadic influence achieves highest accuracy in predicting student relationships.
Federated learning framework extended for personalizing global models.
problem Evaluate personalization strategies for global models on-device.
method Extend federated learning framework, develop tools for analysis and evaluation.
result Personalization yields significant benefits for a large user population.
This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is constructed using an arra…
Adaptive personalized federated learning improves local model personalization.
problem Maximizing global model performance limits local model personalization.
method APFL algorithm trains local models while contributing to global model, with optimal mixing parameter and communication-efficient optimization.
result Demonstrates effectiveness of personalization schema and correctness of generalization theories.
Bayesian optimization generates personalized face stimuli for cognitive neuroscience.
problem Lack of personalized face stimuli in cognitive neuroscience studies.
method Combines GANs with Bayesian optimization to identify individual response patterns to faces.
result Algorithm efficiently generates optimal faces maximizing individual subject's response.