Automates RL for LTV in recommender systems.
problem Maximizing future user gains over time.
method Combines RL with recommendation systems, automating state-space representation.
result Automated RL for LTV maximizes user gains over time.
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arXiv research
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.
169,291 papers · 148 categories
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48 results for “User Gains”
Many users in online social networks are constantly trying to gain attention from their followers by broadcasting posts to them. These broadcasters are likely to gain greater attention if their posts can remain visible for a longer period of time among their followers' most recent feeds. Then when to post? In this pape…
A new framework promotes trustworthy user-generated datasets by ensuring no user benefits from misreporting.
problem Incentivizing data misreporting in user-generated datasets.
method Proposes Licchavi, a global and personalized learning framework with provable strategyproofness guarantees.
result Proves that no user can gain much by replying to Licchavi's queries with deviated answers.
Flexible priors improve VAE-based CF models for better user preference modeling.
problem Simplistic priors in VAEs limit user preference modeling and deeper representation learning.
method Incorporated flexible priors and gating mechanisms into VAEs for collaborative filtering.
result Flexible priors and gating mechanisms significantly improve recommendation performance.
MetaSelector learns to choose the best model for each user.
problem Heterogeneous datasets and user-specific historical data make it hard to find the best model for each user.
method Meta-learning framework to train a model selector that chooses the best model for each user based on their historical data.
result MetaSelector outperforms single model and sample-level model selector in AUC and LogLoss.
This paper examines feature selection for extracting user intentions from Twitter.
problem Extracting user intentions from informal, misspelled tweets.
method Developed a dataset from Twitter feeds, used two feature selection techniques (Information Gain and hybrid forward selection), and applied four classification algorithms.
result The hybrid feature selection approach outperformed the Information Gain method.
ARNN augments RNNs with user-contextual preference for better session-based recommendations.
problem Limited context-awareness in RNN session models.
method Proposes ARNN that uses PNN to extract high-order user-contextual preference.
result ARNN outperforms baseline RNN by a large margin with rich user-side contexts.
GUIDE-VAE generates user-guided data with improved realism and performance.
problem Generating data points for multi-user datasets while considering user information.
method Conditional generative model that integrates user embeddings and a pattern dictionary-based covariance composition.
result GUIDE-VAE outperforms conventional VAEs in multi-user settings, especially under data imbalance.
Proposes efficient user cold start recommendation via meta parameter partition.
problem User cold start in recommendation systems.
method Divides model parameters into fixed and adaptive parts, learning them separately offline and online.
result Significant improvement in AUC (2.48% absolute improvement).
CoExBO optimizes lithium-ion batteries with user input, enhancing trust and efficiency.
problem User distrust in Bayesian optimization due to opacity and lack of user input.
method Preference learning and iterative explanation to integrate user insights.
result Algorithm converges to optimal solution even with adversarial user inputs.
Model user preferences for conversational LLMs using weak rewards.
problem Lack of persistent user models in conversational LLMs leading to repeated user restatements.
method Vector-Adapted Retrieval Scoring (VARS) framework that updates user vectors online from weak scalar rewards.
result Full VARS agent achieves strongest overall performance, matches strong Reflection baseline in task success, and reduces user effort.
Proposes a bandit framework for dynamic user incentives.
problem Designing personalized incentives for users with evolving preferences.
method Combines greedy matching, UCB, and Markov chain theory.
result Algorithm provides theoretical regret bounds and practical examples.
This paper models continuous user experience evolution for better item recommendations.
problem Dynamic user experience in online review communities.
method Combines Geometric Brownian Motion, Brownian Motion, and Latent Dirichlet Allocation to model continuous user experience and language evolution.
result The model outperforms discrete models and state-of-the-art methods in predicting item ratings.
FairJudge identifies fraudulent users in rating platforms.
problem Untrustworthy users giving fraudulent ratings.
method Three metrics: fairness, reliability, and goodness; iterative algorithm to predict these metrics.
result Significantly outperforms existing algorithms in predicting fair and unfair users.
This survey analyzes knowledge discovery in cryptocurrency transactions.
problem Understanding user behaviors and collective actions in cryptocurrency transactions.
method Data mining techniques and literature review.
result Classified existing research into three aspects and discussed major findings.
The paper tackles robust policy learning in multitask contextual bandits with adversarial users.
problem Learning optimal policies in multitask contextual bandits with a small fraction of adversarial users.
method Developed efficient robust mean estimators for both uni-variate and high-dimensional random variables.
result Lower bound of per-user interactions to learn an -optimal policy for good users.
PRINCE provides interpretable explanations for recommender systems by removing minimal user actions.
problem Lack of interpretable explanations for recommender systems.
method PRINCE uses a polynomial-time optimal algorithm based on random walks over dynamic graphs to find minimal user actions that change recommendations.
result PRINCE produces more compact explanations than intuitive baselines and is viable for user understanding.
BED-LLM uses Bayesian experimental design to improve LLMs' information gathering.
problem Improving LLMs' ability to gather information adaptively.
method Iteratively choosing questions to maximize expected information gain using a probabilistic model.
result BED-LLM achieves substantial performance gains compared to other adaptive design strategies.
Paper explores how to use mixed types of side information for better recommendations.
problem Challenges in using heterogeneous side information for recommender systems.
method Proposes a framework to jointly capture flat and hierarchical side information.
result Demonstrates significant performance gains over state-of-the-art methods.
New interface explains contextual bandits to non-experts.
problem Interpreting and managing contextual bandits for non-expert operators.
method Developed a metric 'value gain' for off-policy evaluation and designed an interface to explain bandit behavior.
result Empowered non-experts to manage complex machine learning systems through accessible presentation.
Federated Learning leaks user-specific information, making devices deanonymizable.
problem Federated Learning leaks user-specific information, making devices deanonymizable.
method Identified subtle variations in model updates that encode user-specific data. Proposed data-augmentation strategies to mitigate deanonymization.
result Data-augmentation strategies offer substantial protection against deanonymization threats with little effect on utility.
Estimates user preferences from noisy paired comparisons.
problem Estimating user preferences from noisy paired comparisons.
method Greedy information maximization strategies.
result Superior preference estimation over state-of-the-art methods.
Enhances cooperative multi-task SemCom for distributed users.
problem Performance degradation in cooperative multi-tasking due to negative information transfer.
method Federated learning (FL) with semantic-aware task clustering.
result Constructive cooperation across distributed users with semantic-aware task clustering.
Proposes MCCF to distinguish latent purchasing motivations in user-item interactions.
problem Difficulty in capturing fine-grained user preferences due to complex latent motivations.
method Introduces MCCF with decomposer and combiner modules to identify and recombine latent components.
result Significant performance gains and necessity of considering multiple components demonstrated.
Improved image synthesis with user scribbles and text prompts.
problem Inadequate details in generated images due to domain shift.
method Optimization problem formulation and cross-attention for control.
result Significant improvement in user satisfaction (85.32% higher).
NESA learns user preferences and calendar contexts for efficient event scheduling.
problem Challenges in understanding user preferences and complex calendar contexts for automated event scheduling.
method Leverages deep neural networks to learn user preferences and calendar context from raw online calendars.
result Significantly outperforms previous models in personal and multi-attendee event scheduling tasks.
Model learns individual preferences for photo aesthetics.
problem Lack of personalized aesthetics models in photography.
method Residual learning approach to adapt to individual preferences.
result Surpasses state-of-the-art methods in predicting aesthetic value.
RippleNet uses a knowledge graph to improve recommendation by propagating user preferences.
problem Collaborative filtering sparsity and cold start problem.
method End-to-end framework that propagates user preferences over the knowledge graph.
result Ripple Network achieves substantial gains in recommendation performance.
Aesthetic-based clothing recommendation improves user satisfaction.
problem Lack of aesthetic features in existing clothing recommendation methods.
method Introduce aesthetic features extracted by a neural network and incorporate them into a personalized tensor factorization model.
result Our approach significantly outperforms state-of-the-art recommendation methods.
The paper explores how to measure and optimize ad reach while maintaining user privacy.
problem Measuring ad reach while preserving user privacy in online advertising.
method Introduces -anonymity and probabilistic discounting for frequency capping.
result Privacy introduces a significant performance drop but with manageable costs.
Paper uses user engagement signals to automatically label training data for AI assistants.
problem Lack of annotated training data for AI assistants.
method Leverages user engagement signals for unsupervised entity labeling and data augmentation.
result Significant accuracy gains in sequence labeling tasks and user-facing results.
Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and…
Study on adversarial attacks on user identification systems using motion sensors.
problem Adversarial attacks on deep learning models for user identification based on motion sensors.
method Study of adversarial example generation methods and their impact on user identification systems.
result Deep neural networks trained for user identification based on motion sensors are vulnerable to adversarial attacks, leading to high misclassification rates.
New radar-based method improves multiclass classification of road users, especially in challenging conditions.
problem Accurate classification of multiple road users in challenging scenarios.
method 50 features extracted from radar data, subset chosen, tested on random forest and LSTM classifiers, addressed data imbalance issues.
result Substantial improvements in multiclass classification compared to ordinary methods.
CausalRM models rewards from user feedback, overcoming noise and bias.
problem Aligning language models with user preferences from noisy, biased feedback.
method Causal-theoretic reward modeling framework addressing noise and bias in observational feedback.
result CausalRM learns accurate reward signals from noisy and biased observational feedback.
This paper tackles spam detection on Twitter by analyzing correlated features.
problem Spam detection on social media, especially Twitter, to improve user experience.
method Extracted tweet-based and user-based features, identified correlated features, and used artificial neural networks for classification.
result Achieved 97.57% accuracy in classifying tweets as spam or non-spam.
New algorithm speeds up user preference learning in conversational contexts.
problem Limited performance of existing conversational contextual bandit approaches.
method Proposes ConLinUCB framework and two algorithms, ConLinUCB-BS and ConLinUCB-MCR, with explorative key-term selection.
result Proves tighter regret bounds and achieves significant computational efficiency improvements.
A new algorithm for conversational recommendation systems using dueling bandits in GLMs.
problem Limited user feedback in existing conversational bandit methods.
method Integrates dueling bandits with relative feedback in generalized linear models.
result Theoretical and empirical validation of ConDuel's efficacy.
A deep RL framework optimizes resource allocation in wireless networks.
problem Optimizing resource allocation and interference in wireless networks.
method Multi-agent deep reinforcement learning for distributed decision-making.
result Our approach outperforms decentralized and centralized baselines in terms of user rates.
Adversaries manipulate wireless power allocation to reduce user rates.
problem Adversaries exploit deep learning for power control to decrease communication rates.
method Adversaries craft perturbations to inputs of a DNN to minimize power allocation.
result Adversarial attacks are highly effective and robust to uncertainties.
A new model uses past consumption history to predict user preferences.
problem Predicting user preferences from past consumption history.
method Sequential Variational Autoencoder with a recurrent neural network.
result The model outperforms state-of-the-art methods by significant margins.
Analyzes stock trends and e-commerce user behavior using Twitter data.
problem Understanding the relationship between stock prices, stock news, and e-commerce user behavior.
method Cross-domain analysis using Hadoop, Hive, and Tableau on three datasets.
result Identified correlations between stock sentiment, stock trends, and e-commerce user behavior.
Paper tackles robust federated learning for affine distribution shifts.
problem Statistical heterogeneity and distribution shifts degrade model performance in federated learning.
method Develops a robust federated learning algorithm (FLRA) for affine distribution shifts.
result FLRA achieves significant performance gains against affine distribution shifts.
DKN uses knowledge graphs to improve news recommendation.
problem Limited personalized news recommendations due to lack of external knowledge.
method Integrates knowledge graph representation into news recommendation using a deep knowledge-aware network (DKN).
result DKN achieves substantial gains over state-of-the-art models in click-through rate prediction.
Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awarenesscs.SI
Interactive learning improves real-time tweet classification for situational awareness.
problem Difficulty in identifying relevant tweets from noisy social media data.
method Interactive learning framework that incorporates user feedback in real-time.
result Our approach outperforms state-of-the-art models in real-time tweet classification.
DeepProbe uses seq2seq models to improve query understanding and chatbot design.
problem Improving query understanding and chatbot design efficiency.
method Attention-based seq2seq recurrent neural network for information extraction and active user interactions.
result DeepProbe achieves significant improvements in query understanding and chatbot efficiency.
Study learns cost functions from user behavior using Wasserstein optimization.
problem Learning a user's true cost function from observed behavior.
method Unified KL framework and two-step Wasserstein inverse optimal control.
result Significant performance gains over existing methods in recommender systems and social networks.
RNNs are competitive but not as user-friendly as ETS and ARIMA.
problem Improving RNNs for non-expert users.
method Empirical study and open-source framework of RNN architectures.
result RNNs can model seasonality directly if the series have homogeneous patterns.