A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Operationalizes engagement as human behavior, linking theory and data.
A dataset for evaluating engagement with scientific video lectures.
Study improves engagement prediction in educational videos.
Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.
UAVs use RL to outperform a greedy shooter in 2D engagements.
D2KLab's approach predicts tweet engagement using two stages.
FATE predicts user engagement on social apps with explainable explanations.
The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers includes that of enrolling patients into care programs and keeping them sufficiently engaged in the program, so that patients can attain various goals. The attainmen…
Study analyzes factors influencing healthcare providers' engagement with SMS campaigns.
Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands. However, components in these systems generally rely on supervised machine learning algorithms that require large amounts of hand-annotated training data, which is expe…
Blockchain fan tokens boost sports fan engagement by 50%.
Mathematical models reveal key factors for engaging Gen Z at work.
Study shows impersonators generate fake engagement on Instagram.
While mobile social apps have become increasingly important in people's daily life, we have limited understanding on what motivates users to engage with these apps. In this paper, we answer the question whether users' in-app activity patterns help inform their future app engagement (e.g., active days in a future time w…
We propose a multimodal approach for detection of students' behavioral engagement states (i.e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse. Final behavioral engagement states are achieved by fusing modality-specific classifiers at the decision level. Various…
Topologically and geometrically engaging actions have proved to be useful to obtain rigidity results for semisimple Lie group actions. We show that the action of a simple noncompact Lie group on a compact manifold preserving a unimodular rigid geometric structure of algebraic type (e.g. a connection together with a vol…
The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively u…
To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study. In Phase 1, contextual logs (URLs) are utilized to assess active usage of the content platform. If there is active use, the appearance information is utilized in Phase 2 to infer behavi…
New framework optimizes for 'value' rather than engagement.
Spotify improves content mix using contextual bandits.
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.
Study user engagement in mobile health apps for health workers in resource-poor settings.
The possibility of using player engagement predictions to profile high spending video game users is explored. In particular, individual-player survival curves in terms of days after first login, game level reached and accumulated playtime are used to classify players into different groups. Lifetime value predictions fo…
The ever-increasing demand from mobile Machine Learning (ML) applications calls for evermore powerful on-chip computing resources. Mobile devices are empowered with heterogeneous multi-processor Systems-on-Chips (SoCs) to process ML workloads such as Convolutional Neural Network (CNN) inference. Mobile SoCs house sever…
MUTLA dataset analyzes multimodal data for teaching and learning analytics.
For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e.g., click-through-rate, average engagement time etc.) is crucial. We approach the aforementioned task from a learning-to-rank perspective which reveals a new problem setup. In traditional learning-t…
Estimates users' preference for a site over others using engagement data.
Twitter releases dataset to study user engagement on Home Timeline, focusing on privacy.
Builds a novel educational recommender for lifelong learners.
Data-driven approaches which aim to identify and predict player engagement are becoming increasingly popular in games industry contexts. This is due to the growing practice of tracking and storing large volumes of in-game telemetries coupled with a desire to tailor the gaming experience to the end-user's needs. These a…
Fake engagement is one of the significant problems in Online Social Networks (OSNs) which is used to increase the popularity of an account in an inorganic manner. The detection of fake engagement is crucial because it leads to loss of money for businesses, wrong audience targeting in advertising, wrong product predicti…
Framework detects fake news using weak social signals from multiple sources.
Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs. To understand user intent and tailor recommendations to their needs, we use deep learning to explore various heterogeneous data available in marketplaces. This paper focuses on the challenge of measuring re…
In this paper we present the first results of a pilot experiment in the capture and interpretation of multimodal signals of human experts engaged in solving challenging chess problems. Our goal is to investigate the extent to which observations of eye-gaze, posture, emotion and other physiological signals can be used t…
Study finds different active learning techniques better for various learning objectives.
New approach tackles non-Markovian behavior in maternal health programs.
The paper proposes a survival model to optimize mobile notification delivery times.
Educational game on crypto investment helps students grasp macroeconomics.
New approach to disentangle utility from impulse in recommendation systems.
TrueLearn uses Bayesian algorithms to match learners with educational resources.
LOLA uses LLMs to optimize content delivery, outperforming traditional methods.
Bitcoin volatility can be predicted from price and alternative data.
Human behavior expression and experience are inherently multi-modal, and characterized by vast individual and contextual heterogeneity. To achieve meaningful human-computer and human-robot interactions, multi-modal models of the users states (e.g., engagement) are therefore needed. Most of the existing works that try t…
Study combines chit-chat and goal-oriented dialogue in fantasy games.
Study shows activist board representation improves Japanese companies' performance.
Most practical recommender systems focus on estimating immediate user engagement without considering the long-term effects of recommendations on user behavior. Reinforcement learning (RL) methods offer the potential to optimize recommendations for long-term user engagement. However, since users are often presented with…
Organic updates (from a member's network) and sponsored updates (or ads, from advertisers) together form the newsfeed on LinkedIn. The newsfeed, the default homepage for members, attracts them to engage, brings them value and helps LinkedIn grow. Engagement and Revenue on feed are two critical, yet often conflicting ob…
Decodes neural activity to detect context effects in natural settings.