arXiv research

A new model considers fatigue in online content recommendation systems.

In this paper we consider an online recommendation setting, where a platform recommends a sequence of items to its users at every time period. The users respond by selecting one of the items recommended or abandon the platform due to fatigue from seeing less useful items. Assuming a parametric stochastic model of user …

Motivated by the observation that overexposure to unwanted marketing activities leads to customer dissatisfaction, we consider a setting where a platform offers a sequence of messages to its users and is penalized when users abandon the platform due to marketing fatigue. We propose a novel sequential choice model to ca…

This study proposes an exercise fatigue detection model based on real-time clinical data which includes time domain analysis, frequency domain analysis, detrended fluctuation analysis, approximate entropy, and sample entropy. Furthermore, this study proposed a feature extraction method which is combined with an analyti…

Automated fatigue assessment using ECG and actigraphy sensors.

Self-supervised learning improves RUL prediction with limited data in fatigue damage prognosis.

In online display advertising, selecting the most effective ad creative (ad image) for each impression is a crucial task for DSPs (Demand-Side Platforms) to fulfill their goals (click-through rate, number of conversions, revenue, and brand improvement). As widely recognized in the marketing literature, the effect of ad…

Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue . They are based on the statistical analysis of accumulated and moving window data subsets with construction of a kurtosis-skewness diagram. This approach was applied to the data gat…

Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of event-based signals. However, some spike-timing-related strengths of SNNs are hindered by the sensitivity of spike-timing-dependent plasticit…

Deep Q-learning analyzes EEG for drowsiness during driving tests.

This research develops efficient surrogate models for predicting crack growth in metal structures.

This paper improves Gaussian process predictions by integrating prior knowledge.

Improved aircraft structure prediction using derivative-enhanced sparse Cholesky GP method.

The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the rec…

Slender marine structures such as deep-water marine risers are subjected to currents and will normally experience Vortex Induced Vibrations (VIV), which can cause fast accumulation of fatigue damage. The ocean current is often three-dimensional (3D), i.e., the direction and magnitude of the current vary throughout the …

Method screens similar capsule endoscopic images, reducing doctor workload and improving accuracy.

Proposes a new MOBO framework for better estimation of uncertain parameters in expensive black-box functions.

Model trains passing events on a bridge using multilevel Gaussian process.

In many instances, information on engineering systems can be obtained through measurements, monitoring or direct observations of system performances and can be used to update the system reliability estimate. In structural reliability analysis, such information is expressed either by inequalities (e.g. for the observati…

The paper develops a reinforcement learning model to estimate ad impact considering delayed and cumulative effects.

In many contexts the modal properties of a structure change, either due to the impact of a changing environment, fatigue, or due to the presence of structural damage. For example during flight, an aircraft's modal properties are known to change with both altitude and velocity. It is thus important to quantify these cha…

GUIDE-VAE generates user-guided data with improved realism and performance.

Hybrid approach combines user feedback and machine learning for predicting user satisfaction.

Author2Vec generates user embeddings from social media data.

Optimal recommendation system using user and item clustering.

CnGAN generates synthetic user preferences for non-overlapped users in cross-network recommender systems.

Recommender systems recommend items more accurately by analyzing users' potential interest on different brands' items. In conjunction with users' rating similarity, the presence of users' implicit feedbacks like clicking items, viewing items specifications, watching videos etc. have been proved to be helpful for learni…

The paper proposes a method to learn user representations invariant to social media behavior changes.

New method detects and measures malicious users in recommendation algorithms.

Proposes CF-SFL to improve sparse data recommendation.

A multi-user multi-armed bandit (MAB) framework is used to develop algorithms for uncoordinated spectrum access. The number of users is assumed to be unknown to each user. A stochastic setting is first considered, where the rewards on a channel are the same for each user. In contrast to prior work, it is assumed that t…

Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received fee…

We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. Each user may be recommended a given item at most once. A latent variable model specifies the user preferences: both users and items are clustered into types. Al…

New policy reduces spectrum access regret in uncoordinated systems.

Paper uses RNN to predict SaaS user lifetime value.

Deep learning diagnoses MS from smartphone data.

The paper tackles robust policy learning in multitask contextual bandits with adversarial users.

Improved neural model for social recommendation by integrating social and interest networks.

In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with…

In human-in-the-loop machine learning, the user provides information beyond that in the training data. Many algorithms and user interfaces have been designed to optimize and facilitate this human--machine interaction; however, fewer studies have addressed the potential defects the designs can cause. Effective interacti…

Study personalizes user experience to maximize rewards with patience budget.

Estimates population mean from user-level data with privacy, accounting for heterogeneity.

Deep learning predicts user identity, activity, and location from Wi-Fi signals.

AI2V learns user representations by focusing on recent interests.

Model user preferences for conversational LLMs using weak rewards.

Bayesian optimization agent learns user preferences from pairwise comparisons.

Proposes a graph neural network for personalized news recommendation.

MRIF models dynamic user interests at multiple temporal-ranges.