A novel RNN survival model predicts web user return times.
problem Predicting when web users will return.
method Developed a novel RNN survival model that combines RNN's feature learning with survival analysis's non-returning user representation.
result Successfully predicts return times with superior discrimination between returning and non-returning users.
ChOracle predicts user return times to improve churn prediction.
problem Churn prediction in online services.
method Combining Temporal Point Processes and Recurrent Neural Networks with latent variables.
result Superior performance on various real-world datasets.
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.
CrystalCandle creates user-friendly explanations for machine learning models.
problem Low trust in predictive models due to lack of interpretability.
method End-to-end pipeline for model interpretation, including Model Importer, Interpreter, Narrative Generator, and Exporter.
result CrystalCandle leads to higher adoption rates and improved downstream metrics.
The paper predicts TSE stocks using social media sentiment and volume.
problem Predicting Tehran Stock Exchange (TSE) variables using social media data.
method Hybrid sentiment analysis combining lexicon-based and learning-based methods; built a sentiment lexicon for Persian language.
result Sentiment and volume of online comments are useful for predicting TSE stocks.
This survey reviews portfolio choice in settings where investment opportunities are stochastic due to, e.g., stochastic volatility or return predictability. It is explained how to heuristically compute candidate optimal portfolios using tools from stochastic control, and how to rigorously verify their optimality by mea…
A method learns user preferences for better personalized portfolios.
problem Difficult balancing of portfolio factors like returns, risk, exposure, etc.
method Active preference learning using Bayesian optimization.
result Personalized portfolios are identified that are distinct from user perspective.
The paper improves recommendation systems by ensuring their outputs are reliable.
problem Recommendation systems often lack reliability guarantees for their outputs.
method The method uses a pre-trained ranking model to create a set of items with rigorous FDR control.
result The approach provides a way to guarantee the reliability of recommendation outputs.
MILLION framework optimizes portfolio risk and return efficiently.
problem Optimizing risk and return in AI for FinTech portfolio management.
method Two phases: return maximization with auxiliary objectives and risk control with portfolio interpolation and improvement.
result Framework achieves fine-grained risk control and improved return rates.
The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semant…
Method certifies edge predictions with cloud-level reliability.
problem Ensuring reliability of edge intelligence models.
method Conformal alignment-based cascading mechanism.
result Certifies conditional coverage with user control over risk level.
Choppy optimizes ranked list truncation using Transformer architecture.
problem Optimal truncation of ranked search results to balance relevance and user cost.
method Assumption-free Transformer model optimizing user-defined IR metrics.
result Choppy improves upon recent state-of-the-art methods.
DynForest R package predicts outcomes with time-dependent predictors.
problem Handling time-dependent predictors in random forest models.
method Random forests with time-dependent predictors summarized using flexible linear mixed models.
result DynForest can predict continuous, categorical, and survival outcomes.
New approach to disentangle utility from impulse in recommendation systems.
problem Difficulty in inferring user utility from engagement signals.
method Generative model based on self-exciting Hawkes process to infer utility from return probability.
result It is possible to disentangle System-1 and System-2 decision processes to optimize content based on user utility.
Model uses LLM features to predict stock returns effectively.
problem Predicting stock returns from text data.
method Structured Event Representation (SER) model with attention mechanisms.
result SER-based model outperforms existing models in stock return prediction.
Paper uses RNN to predict SaaS user lifetime value.
problem Predicting user lifetime value in SaaS applications.
method Recurrent Neural Network with multi-cell architecture, accounting for cohort, age-in-system, and contemporaneous information.
result Significantly improved prediction accuracy compared to existing models.
Predicts user session length in streaming services with hierarchical modeling and shrinkage.
problem Predicting user session length in streaming services is challenging due to external factors and lack of covariates.
method Inspired by hierarchical Bayesian modeling, the approach incorporates flexible parametric/nonparametric models and uses hierarchical shrinkage.
result The method outperforms state-of-the-art estimators in efficiency and predictive performance.
Deep learning predicts user identity, activity, and location from Wi-Fi signals.
problem Privacy concerns and need for non-invasive user authentication, activity classification, and tracking.
method End-to-end deep learning framework using passive Wi-Fi signals.
result System autonomously predicts user identity, activity, and location without user intervention.
A framework for private prediction sets using conformal prediction and differential privacy.
problem Jointly addressing reliability and privacy in machine learning predictions.
method Split conformal prediction with privatized quantile subroutine.
result Private prediction sets can be generated from privately-trained models.
Bayesian nonparametric model predicts user activity and intervention success.
problem Predicting user activity and intervention success in online experiments.
method Bayesian nonparametric approach to model user heterogeneity and derive user activity predictions.
result The proposed method outperforms existing approaches in predicting user activity and intervention success.
The paper examines how different fusion strategies in neural networks affect user embeddings and their quality.
problem The need for automated processing of user data, particularly in predicting ratings and estimating user similarity.
method Analyzed the effect of various fusion strategies in neural networks on user embeddings quality and prediction performance.
result Fusion strategies in neural networks affect both embedding quality and prediction performance, and prediction performance does not necessarily reflect embedding quality.
Proposes a deep learning model to predict user attrition.
problem Attrition prediction in user activity logs.
method End-to-end learning scheme integrating logs and profiles.
result Offers superior performance compared to existing methods.
RNNs improve predictive precompute for faster user interface response times.
problem Improving user interface response times in mobile and web applications.
method Application of recurrent neural networks (RNNs) for predictive precompute.
result RNN models improve prediction accuracy and reduce computational cost.
EXAMM evolves RNNs for stock return prediction and portfolio trading.
problem Predicting stock returns for optimal portfolio trading.
method Evolutionary Neural Architecture Search (EXAMM) for evolving RNNs.
result Evolving RNNs outperform traditional benchmarks in stock trading.
Method fuses transaction data for better demographic prediction.
problem Inference of user demographic attributes is crucial but sensitive.
method Embedding-based approach integrating multifaceted transaction data and auxiliary tables.
result Improved demographic prediction accuracy.
Paper proposes a combined model for better recommendation by integrating explicit and implicit feedbacks.
problem Improve recommendation accuracy by considering both explicit and implicit feedbacks.
method Developed three models (RHC-PMF, RV-PMF, RHCV-PMF) that incorporate users' explicit and implicit feedbacks for better rating prediction.
result RHCV-PMF model outperforms other models in cold start scenarios for both users and items.
Bayesian approach confirms no return predictability for 1926-2004 data, weak evidence for 1953-2021.
problem Investigating return predictability using Bayesian methods.
method Developed a new shrinkage type prior for a model parameter in a VAR system, compared to other estimation methods.
result Bayesian approach outperforms reduced-bias estimator in terms of size and power.
Paper predicts user interests from browsing history and event sequences.
problem Capturing subtle user interests and inter-personal influence.
method Deep prediction method based on two RNNs modeling temporal point process and attention mechanism.
result Model outperforms state-of-the-art methods in fine-grained user interest prediction.
Regularizes predictions to encourage beneficial user actions.
problem Learning models affect user actions, but provide no guarantees.
method Introduces look-ahead regularization to anticipate and encourage beneficial actions.
result Effective in promoting beneficial user actions in real and synthetic data.
There is a large amount of interest in understanding users of social media in order to predict their behavior in this space. Despite this interest, user predictability in social media is not well-understood. To examine this question, we consider a network of fifteen thousand users on Twitter over a seven week period. W…
The paper uses action graphs to predict user engagement in Snapchat.
problem Understanding what motivates users to engage with mobile social apps.
method Formalized in-app action transition patterns as action graphs, analyzing their characteristics to predict future engagement.
result Action graphs can characterize user behavior patterns and inform future engagement.
Study proposes a time-aware model to predict user conversion intent.
problem Weak predictive signals from users not suitable for conversion prediction.
method Time-aware approach to model user activities and capture conversion intent signals.
result Approach outperforms other models on real-world datasets.
Predicts asset return distributions using LSTM and quantile regression.
problem Predicting complex asset return distributions.
method Two-stage approach: quantile prediction using asset-specific features, market data adjustment.
result Significantly outperforms existing models (98% improvement over baseline).
ESN models predict intraday stock returns efficiently.
problem Intraday stock return prediction using machine learning.
method Echo State Network (ESN) models with random parameters.
result ESN models achieve strong forecasting performance efficiently.
FATE predicts user engagement on social apps with explainable explanations.
problem Accurate user engagement prediction for social apps with explainability.
method FATE, a flexible neural framework incorporating friendships, actions, and temporal dynamics.
result FATE outperforms state-of-the-art approaches by 10% error and 20% runtime reduction.
Artificial Neural Networks predict stock returns, finding larger stocks less predictable.
problem Evaluating the validity of the Efficient Market Hypothesis.
method Backpropagation Artificial Neural Network analysis of Brazilian stock market.
result Predictability of stock returns is related to market capitalization, with larger stocks less predictable.
Deep learning predicts semitransparent pigment mixtures for novice painters.
problem Support novice painters in learning color mixing.
method Built a watercolor dataset with transmittance and reflectance data. Used a deep neural network to train a model for predicting pigment mixtures.
result Trained model accurately predicts semitransparent pigment mixtures.
Paper proposes TPathMine model for more accurate user attribute prediction.
problem Predicting user attributes from click data in heterogeneous networks.
method HetPathMine model with meta-path weights optimized for user emotional preferences.
result TPathMine model achieves higher accuracy in user attribute prediction.
Behavior modification improves prediction accuracy by nudging user behavior.
problem Improving prediction accuracy using behavior modification techniques.
method Combining prediction and behavior modification with reinforcement learning algorithms.
result Behavior modification can make predictions more certain but may not generalize.
Bayesian method combines expert and user rankings using copulas.
problem Combining expert and user rankings for accurate predictions.
method Bayesian inference with copula modeling latent variables.
result Predictive distribution of user rankings can be approximated accurately.
Study finds no consistent return predictability using payout ratios across 16 countries.
problem Return predictability using payout ratios in various markets.
method Analysis of 16 developed countries' bond, equity, and housing markets using payout-price ratios.
result No consistent in-sample and out-of-sample performance with positive utility gain.
Predicts user age and gender on Tumblr using rich content.
problem Challenges in targeting specific demographic groups on Tumblr.
method Graph based and deep learning models, including network embedding, label propagation, CNN, and MLP.
result Significantly improved accuracy for age and gender predictions.
Interactive learning explained to users improves trust and model understanding.
problem Lack of user understanding and trust in interactive learning models.
method Proposes a framework where learners explain interactive queries and predictions to users, using visual explanations.
result Boosts predictive and explanatory powers of and user trust in learned models.
We introduce a novel latent grouping model for predicting the relevance of a new document to a user. The model assumes a latent group structure for both users and documents. We compared the model against a state-of-the-art method, the User Rating Profile model, where only users have a latent group structure. We estimat…
This paper proposes an embedding-based neural network for more accurate investment return prediction.
problem Accurately predicting investment returns requires understanding industry knowledge and news, as well as leveraging relevant theories.
method The approach uses embedding to encode investment IDs into low-dimensional vectors, leveraging dual branches to separate different information, and employs the swish activation function.
result The proposed embedding-based dual branch model outperforms traditional machine learning models like Xgboost, Lightgbm, and Catboost on the Ubiquant Market Prediction dataset.
Agent predicts financial returns and volatility with high success rate.
problem Financial market prediction and volatility estimation.
method Modular networked learning system with interconnected recurrent neural networks.
result Agent predicts financial returns and volatility with over 80% success rate.
Intangible investment becomes a strong predictor of stock returns over time.
problem Understanding the role of intangible investment in stock returns over different periods.
method Comparing intangible investment's predictive power over two distinct periods (1963-1992 and 1993-2022) using orthogonal factors.
result Intangible investment's predictive power for stock returns has significantly increased over time, becoming a main predictor for recent periods.
JODIE learns dynamic user-item embeddings from interactions, outperforming existing methods.
problem Modeling dynamic user-item interactions for accurate future predictions.
method JODIE uses coupled recurrent models with update, projection, and prediction components, and a novel t-Batch algorithm.
result JODIE outperforms state-of-the-art methods by up to 22.4% on future interaction and state change prediction tasks.