TIM framework uses LLMs and domain experts to infer DeFi user transaction intents.
problem Challenges in understanding user intent in DeFi transactions due to complex interactions and opaque logs.
method TIM framework leverages a DeFi intent taxonomy, multi-agent LLM system, and a Meta-Level Planner.
result TIM significantly outperforms existing methods in inferring user transaction intents.
Despite the growing importance of multilingual aspect of web search, no appropriate offline metrics to evaluate its quality are proposed so far. At the same time, personal language preferences can be regarded as intents of a query. This approach translates the multilingual search problem into a particular task of searc…
Intelligent recommender system tracks user activity and intent for better recommendations.
problem Recommender systems often lack user intent awareness, leading to suboptimal recommendations.
method Encoded user activity, reduced to lower dimensions using tensor factorization, and scored for intent. Combined with contextual information for ranking recommendations.
result Better recommendations compared to baselines, with intent-aware scoring.
Paper addresses bias in search intent affecting click behavior.
problem Bias in user search intent affects click behavior and relevance.
method Proposes a search intent bias hypothesis to improve click models.
result Click models can better interpret user clicks and improve retrieval performance.
TPG-DNN predicts user intent using GRU loss and multi-task learning.
problem Improving user experience and shopping efficiency in e-commerce.
method Adaptive GRU loss function with multi-task learning.
result TPG-DNN outperforms existing CTR models on Taobao datasets.
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.
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.
New method infers intent from suboptimal behavior by modeling incorrect internal beliefs about dynamics.
problem Inferring intent from suboptimal human behavior using traditional methods assumes near-optimality, which is not always valid.
method Model suboptimal behavior as internal model misspecification, estimating incorrect beliefs about dynamics.
result Accurately models human intent by accounting for internal model inaccuracies.
SYNTHONY selects tabular synthesizers based on stress profiling and user intent.
problem Non-uniform performance of tabular generative models across datasets.
method Stress profiling and intent-conditioned tabular synthesis selection.
result Meta-features predict synthesizer performance, improving selection accuracy.
End-to-end dialogue model learns from joint embeddings and user intent.
problem Challenges in reasoning and incorporating state-full knowledge in goal-oriented dialogues.
method Proposes an RNN-based end-to-end encoder-decoder architecture trained with joint embeddings and multi-task learning.
result Improves task-oriented dialogue system performance as shown by BLEU score evaluation.
Study predicts adolescents' intention to smoke cigarettes using ML models.
problem Early identification of adolescents' smoking intentions.
method Five machine learning algorithms tested for predicting intention to smoke cigarettes.
result Gradient Boosting Classifier showed highest accuracy.
MACRo-mIcro VAE learns disentangled user behavior representations.
problem Complex user behavior data in recommender systems are entangled; disentangling them enhances robustness and interpretability.
method MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE) infers high-level user intentions and micro-disentangles preferences.
result Our approach achieves substantial improvement over state-of-the-art baselines and demonstrates interpretable and controllable learned representations.
Paper tackles privacy in active learning for sensitive data.
problem Privacy leak in active learning with sensitive data.
method Privacy-preserving active learning approach with quantifiable guarantees.
result Tradeoff between privacy, utility, and annotation budget demonstrated.
Improved financial VA intent classification accuracy.
problem Determining user intents for unseen open intents.
method Supervised pre-training of intent representations using prefix-tuning and fine-tuning.
result 1.63% - 2.07% higher accuracy on banking77 benchmark.
Adaptive system diversifies and personalizes visual browsing for better user engagement.
problem Poor performance of search queries in ambiguous or inspirational browsing scenarios.
method Bayesian regression for scoring relevance, submodular diversification, and personalized category preferences learned from user behavior.
result Significant improvement in click-through-rate and session duration on live traffic.
Model combines user preferences and side information for better recommendation.
problem Addressing data sparsity and improving user intent representation.
method Tensor-based model that fuses user preferences with side information.
result Demonstrates effectiveness on standard benchmark datasets.
The paper shows how machine learning models can be fooled by fake users.
problem How machine learning recommendation models can be fooled by fake users.
method A framework for generating fake user profiles that mimic real users and achieve adversarial intent.
result Machine learning recommendation models can be easily fooled by fake users.
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
PIP-Net predicts pedestrian crossing intentions with up to 4-second lead.
problem Accurate pedestrian intention prediction for autonomous vehicles in real-world scenarios.
method Recurrent and temporal attention-based model using kinematic and spatial features.
result PIP-Net predicts pedestrian crossing intentions up to 4 seconds in advance.
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recomme…
Paper models MOOC users behavior using MDP for easier classification.
problem Difficulty in defining and classifying MOOC users based on their behavior.
method Uses Markov Decision Process (MDP) to model user behavior from log data.
result Classifies users based on their intentions inferred from MDP rewards.
Researchers analyze how RNNs solve intent detection tasks using dynamical systems theory.
problem Understanding the internal mechanisms of RNNs in intent detection.
method Investigating RNN architectures through a dynamical systems perspective.
result Identified fixed point topology and limited number of attractors in RNN dynamics.
DSPN predicts advertiser satisfaction and intent for e-commerce platforms.
problem Understanding advertiser intent and satisfaction for e-commerce platforms.
method Two-stage Deep Satisfaction Prediction Network (DSPN) that models intent and satisfaction.
result DSPN outperforms state-of-the-art baselines and predicts advertiser satisfaction accurately.
Neural network predicts purchasing intent without feature engineering.
problem Predicting purchasing intent in ecommerce with minimal feature engineering.
method Trainable vector spaces, multi-layer recurrent neural networks, parameter sharing, skip connections.
result Classification accuracy exceeds state-of-the-art on benchmark datasets.
This paper tackles tweet classification by identifying purpose and position.
problem Difficulties in determining user intention and attitude in short, informal tweets.
method Transformed tweet classification into a multi-label problem and applied a multi-label classification method with post-processing.
result The method effectively classifies tweet purpose and position, outperforming individual classification methods.
Adapts large transformer model for search query intent understanding.
problem Understanding and predicting user intents from search queries.
method Adapts BERT-like architecture for search queries, accounts for query noisiness and sparseness.
result Builds a shareable deep learning model for query intent identification.
Model improves email-based conversational agents' ability to extract relevant information.
problem Asynchronous email communication makes it hard for agents to detect intents and extract relevant entities.
method Neural model for scoping relevant information from large queries.
result Improves performance of intent detection and entity extraction tasks by 35% in precision.
We propose a robust classifier to predict buying intentions based on user behaviour within a large e-commerce website. In this work we compare traditional machine learning techniques with the most advanced deep learning approaches. We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a su…
Estimates treatment effects with machine learning using instruments in A/B tests.
problem Estimating heterogeneous treatment effects with unobserved confounders in A/B tests.
method Develops a statistical learning approach using machine learning methods and auxiliary models.
result Shows robustness of estimated effect model to auxiliary model errors and provides asymptotic normality for parameter estimates.
Seq-CVAE learns a latent space for each word position to capture sentence intention.
problem Capturing diversity in image captioning models.
method Seq-CVAE learns a sequential latent space for each word position, mimicking future sentence summaries.
result Significantly improves diversity metrics on MSCOCO dataset compared to baselines.
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.
Paper improves Native ads CTR prediction using event embeddings and recurrent networks.
problem Hard CTR prediction for Native ads due to lack of direct query intent.
method Proposes a large-scale event embedding scheme and a recurrent neural network model.
result Significantly outperforms baseline and variants in CTR prediction.
Deep neural networks improve recommendation accuracy in marketplaces.
problem Measuring and optimizing recommender performance in marketplaces.
method Hybrid item representation models, sequence-based models, and multi-armed bandit models.
result Promising deep neural network recommenders are currently in production at FINN.no.
Proposes a new method for intent classification in student and enrollee questions.
problem Challenges of intent classification in specific subject areas with limited training data.
method Uses Shannon entropy to generate low-dimensional question vectors.
result Outperforms existing methods in intent classification task with small datasets.
Examines how algorithms affect user autonomy and information choice.
problem Impact of algorithmic recommendations on user autonomy and free choice.
method Double dichotomy analysis of user intentions and actions, prior and posterior information rearrangement.
result Algorithms can expand or limit user cognitive and social horizons.
Solves reinforcement learning reward design for user-aligned agents.
problem Designing effective reward functions for reinforcement learning.
method Reward modeling: learn from user interaction and optimize.
result Agents can be aligned with user intentions through learned reward functions.
New BMI interface stabilizes user experience over long periods.
problem Inconsistent neural signal data leads to interface recalibration.
method Adversarial Domain Adaptation Network to match residual distributions.
result Adversarial Domain Adaptation Network outperforms other methods.
Hybrid approach combines user feedback and machine learning for predicting user satisfaction.
problem Measuring user satisfaction in large-scale conversational agent systems.
method Fusion of explicit user feedback and predictions from two machine-learned models trained on different data types.
result Hybrid approach significantly improves user satisfaction predictions.
UCFE benchmarks LLMs in financial tasks with human feedback.
problem Evaluating LLMs' financial task performance and user satisfaction.
method Hybrid approach combining human expert evaluations and dynamic interactions.
result Significant alignment between benchmark scores and human preferences (Pearson correlation coefficient of 0.78).
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.
Deep learning improves recommendation systems for marketplaces.
problem Challenges in matrix factorization for marketplaces.
method Hybrid recommender system combining user-generated contents and user behavior data.
result Five lessons learned from deep learning experiments.
Robots learn intentions from multiple cues to reduce uncertainty.
problem Uncertainty in human-robot interaction for vulnerable users.
method Multimodal classifier fusion using Bayesian Independent Opinion Pool.
result Fused classifiers outperform individual modalities in accuracy and uncertainty reduction.
Visual reranking is effective to improve the performance of the text-based video search. However, existing reranking algorithms can only achieve limited improvement because of the well-known semantic gap between low level visual features and high level semantic concepts. In this paper, we adopt interactive video search…
New model learns coupled representations for domains, intents, and slots.
problem Representation learning for domains, intents, and slots in spoken language understanding.
method Proposes a model that learns coupled representations by aggregating slot and intent representations based on their hierarchical relationships.
result Improved performance on contextual cross-domain reranking task.
New method adapts to user preferences dynamically, improving recommendation models.
problem Current recommendation models lack dynamic adaptation to changing user preferences.
method Preference Discerning with LLM-Enhanced Generative Retrieval
result Mender achieves state-of-the-art performance in adapting to evolving user preferences.
This work recommends personalized search stories to users based on their interests.
problem Personalized search story recommendation within search engines.
method Deep reinforcement learning architecture trained by imitation learning and reinforcement learning.
result Empirically demonstrated effectiveness on real-world data sets.
Proposes a model to recommend products at the right time to meet user demands.
problem Maximizing product sales by recommending products at the right time to meet user demands.
method Integrates user interests and time-based demands into a Long-Short Demands-Aware Model (LSDM) using recurrent neural networks.
result Demonstrates the effectiveness of the LSDM in next-item recommendation on real-world commerce datasets.
Deep neural network detects driver intentions from video.
problem Detecting driver intentions for safer self-driving.
method Uses deep learning to analyze turn signals and emergency flashers.
result High per-frame accuracy in challenging scenarios.