SR-GNN models sessions as graphs to predict user actions.
problem Predict user actions based on anonymous sessions.
method SR-GNN models sessions as graphs and uses attention networks.
result SR-GNN outperforms state-of-the-art methods consistently.
On-device research index
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.
169,341 papers · 148 categories
Trend · papers per month
48 results for “session recommendation”
ASARS integrates temporal dynamics from CF into session-based RNN for better personalized recommendations.
problem Discarding long-term data across sessions in session-based RNNs.
method ASARS framework that combines attentional network and inter-session temporal dynamic model.
result ASARS improves personalized recommendation performance on four real datasets.
CHAMELEON uses RNNs to recommend news sequences better than other methods.
problem Improving news recommendation accuracy and catalog coverage.
method Hybrid meta-architecture CHAMELEON with RNNs for sequence modeling and side information.
result Significantly higher recommendation accuracy and catalog coverage.
A deep learning architecture for news session-based recommendations.
problem Challenges in news recommendation systems, including sparse user profiling and dynamic user preferences.
method Hybrid approach combining text and metadata features, session-based recommendations with Recurrent Neural Networks, and temporal offline evaluation.
result Significant improvement in top-n accuracy and ranking metrics (10% Hit Rate and 13% MRR) over best benchmark methods.
A hybrid approach uses RNNs to recommend news articles based on context and session history.
problem Challenging news recommendation due to varying user interests and factors.
method Context-aware, hybrid, deep learning approach using RNNs with additional information types.
result Significantly higher recommendation accuracy and catalog coverage compared to other session-based algorithms.
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.
Model learns product vectors from baskets and browsing sessions for better complementary product recommendations.
problem Inferring complementary products from basket and browsing data.
method Proposes BB2vec model that learns product vectors from both baskets and browsing sessions.
result The BB2vec model improves complementary product recommendations and alleviates the cold start problem.
ADER addresses continual learning in session-based recommendation by periodically replaying exemplars with adaptive distillation.
problem Catastrophic forgetting in continual learning of session-based recommenders.
method Periodically replaying previous training samples (exemplars) with an adaptive distillation loss.
result ADER consistently outperforms other continual learning techniques and even all historical data at every update cycle.
The paper tackles biases in session-based recommender systems by modeling user interest as a stochastic process.
problem Data uncertainty, popularity bias, and exposure bias in session-based recommender systems.
method The paper proposes treating user interest as a stochastic process in the latent space, debiasing item embeddings, modeling dense user interest, and introducing fake targets to simulate extended exposure.
result The proposed approach mitigates challenges in session-based recommender systems, as shown by computational experiments on various datasets.
New method for cold-start users in content recommendation systems.
problem Handling new users in content recommendation systems.
method Formulates an optimization problem to maximize session length, using MDP with greedy value iteration.
result Proves the problem has monotone and submodular properties, enabling efficient solution.
Improves session-based recommendation by modeling temporal dynamics.
problem Data sparsity in session-based recommendation.
method Recurrent Latent Variable Networks (RLVN) with variational inference.
result Effective in scaling to large real-world datasets and improving recommendation accuracy.
A new model learns preferences incrementally without personal data.
problem Incremental session-based recommendation without personal data.
method Memory Augmented Neural model (MAN) that combines a neural recommender with a nonparametric memory.
result MAN consistently outperforms existing methods in incremental session-based recommendation.
CHAMELEON tackles news recommendation using deep learning, addressing cold-start issues.
problem Personalizing user experiences in a dynamic news search space.
method Modular reference architecture with different neural building blocks, leveraging user and article context, and modeling temporal decay and concept drift.
result CHAMELEON outperforms traditional and state-of-the-art session-based recommendation algorithms in accuracy, item coverage, novelty, and reduced item cold-start problem.
CLEAR learns causal graphs from attention in recommender systems to explain user behavior.
problem Understanding why specific recommendations are made in recommender systems.
method CLEAR learns session-specific causal graphs from attention in pre-trained neural recommenders, addressing latent confounders.
result CLEAR provides counterfactual explanations that are shorter and more effective than naive methods.
A new CNN model predicts next items in user sessions.
problem Modeling long-range dependencies in item sequences is challenging.
method Introduced a simple yet effective generative model with a stacked 'holed' convolutional layer architecture and residual blocks.
result The model achieves state-of-the-art accuracy with less training time.
RNNs excel at both short-term and long-term user interaction prediction.
problem Modeling user preferences over short and long time horizons.
method Evaluating RNN-based models on both short-term and long-term recommendation tasks.
result RNNs can predict immediate and distant user interactions.
Many-to-one RNN predicts user hotel clicks from browsing history.
problem Predicting user hotel clicks from browsing history.
method Combines rule-based algorithm with Gated RNN to sort accommodations.
result Promising results but computationally demanding.
Model predicts next destination for users based on past trips and features.
problem Predicting the next destination in multi-destination trips.
method Used Cleora for city graph embedding and EMDE for prediction.
result Achieved 2nd place in Booking Data Challenge.
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.
ragamAI uses machine learning to create concert recitals for Carnatic music.
problem Creating a comprehensive listening experience for Carnatic music concerts.
method Playlist and session-based recommender models, leveraging mathematical structure in past concerts.
result ragamAI generates concert recitals that perform 25%-50% better than baseline 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.
Calendar graph neural networks model user behavior with location and time data.
problem Modeling user behavior with location and time information for demographic prediction.
method Graph neural networks with a tripartite network of items, sessions, and locations, and a hierarchical calendar network.
result User embeddings preserve spatial and temporal patterns of various periodicity.
New model for personalized online advertising with multi-user interaction.
problem Realistic online advertising scenarios with multiple users interacting simultaneously.
method Introduces Multi-User Contextual Cascading Bandit (MCCB) model and proposes UCBBP and AUCBBP algorithms.
result Proves UCBBP and AUCBBP achieve optimal regret bounds for multi-user context.
Paper tackles cold-start problems in online recommendation with few-shot learning and meta learning.
problem Cold-start problems in practical recommendations with limited interaction data.
method Combines scenario-specific learning with sequential meta-learning to create an integrated end-to-end framework.
result Significant gains over state-of-the-arts for cold-start problems in online recommendation.
EMDE efficiently estimates manifold densities for diverse recommendation systems.
problem Efficiently estimating manifold densities for multi-modal recommendation systems.
method EMDE (Efficient Manifold Density Estimator) framework for arbitrary vector representations.
result Established new state-of-the-art results in top-k and session-based recommendation settings.
Split-Session Cluster GARCH for Overnight and Intraday Returns: The Role of Tail Heterogeneityecon.EM
The Split-Session Cluster GARCH model captures tail heterogeneity in overnight and intraday returns.
problem Capturing tail behavior and dependence in multivariate asset returns.
method Convolution- distributions, session and sector clustering, block-structured correlation matrices.
result Session-specific and sector-level tail parameters improve model fit and out-of-sample performance.
Brain signal variability in the measurements obtained from different subjects during different sessions significantly deteriorates the accuracy of most brain-computer interface (BCI) systems. Moreover these variabilities, also known as inter-subject or inter-session variabilities, require lengthy calibration sessions b…
This study assesses the reproducibility of 1H-MRS scans across different vendors and sessions.
problem Lack of harmonization in magnetic resonance spectroscopy protocols among vendors.
method Analysis of CV and ICC for within- and between-sessions, and correlation coefficients for across machines.
result Metabolite concentrations are highly reproducible across different vendors and sessions.
Framework for optimizing search engine rankings using observational data.
problem Optimizing ranking policies for search engines using limited observational data.
method Formulated expected reward optimization problem, estimated context value distribution, trained ranking policy via Bayesian inference.
result Demonstrated trade-offs in ranking policies trained on empirical reward estimates.
DCE learns customer embeddings from digital activity and financial context.
problem Comprehensive customer understanding in financial services.
method Leverages customers' digital activity and financial context to learn dense representations.
result DCE showed performance lift in three prediction problems.
Speaker diarization with session-level speaker embedding refinement using graph neural networkseess.AS
Graph neural networks refine speaker embeddings for better session-level diarization.
problem Local speaker distinction in meeting sessions using deep embeddings.
method Graph Neural Networks (GNNs) refine speaker embeddings using session-level structural information.
result Spectral clustering on refined embeddings outperforms original embeddings significantly.
Adversarial deep learning improves EEG-based person identification.
problem Exploiting temporally correlated structures and session variability in EEG data.
method Adversarial inference approach to learn session-invariant representations.
result Improvements in person identification robustness from longitudinal EEG data.
Machine learning improves learning and memory retention by optimizing study sessions.
problem Improving learning and memory retention methods for factual material.
method Large-scale randomized controlled trial with machine learning optimization of study sessions.
result Study sessions optimized with machine learning lead to 67% longer retention and 50% higher return rate.
GAME improves matrix completion by considering subgroup-specific latent structures.
problem Heterogeneous data with overlapping categories, smoothing away subgroup-specific variation.
method Group-Aware Matrix Estimation (GAME) with overlapping nuclear-norm penalties.
result GAME outperforms global low-rank estimators in structured missingness regimes.
We analyze realized volatilities constructed using high-frequency stock data on the Tokyo Stock Exchange. In order to avoid non-trading hours issue in volatility calculations we define two realized volatilities calculated separately in the two trading sessions of the Tokyo Stock Exchange, i.e. morning and afternoon ses…
We study a simple exchange model in which price is fixed and the amount of a good transferred between actors depends only on the actors' respective budgets and the existence of a link between transacting actors. The model induces a simply-connected but possibly multi-component bipartite graph. A trading session on a fi…
Proposes a modified PLDA model for multi-channel conversations.
problem Speaker recognition with simultaneous recordings over multiple channels.
method Modifies PLDA to account for two types of inter-session variability.
result Improved speaker recognition performance with multi-channel data.
Master thesis applies deep learning to sEMG hand gesture recognition, improving accuracy.
problem Reliability issues in sEMG-based hand gesture recognition due to motion artefacts and variability.
method Used deep learning on Unibo-INAIL dataset, collecting data over 8 sessions of 7 subjects.
result Deep learning architecture yields 81.2% inter-posture test accuracy and 75.9% inter-day test accuracy.
This study compares different data preprocessing methods for gait analysis.
problem Improving the generalizability of machine learning models in gait analysis.
method Compared various data preprocessing steps including filtering, time derivative, normalization, and scaling.
result Different preprocessing combinations affect gait classification performance.
This survey was compiled from lectures and problem sessions at the International Conference on Geometric Topology at the Mathematical Research and Conference Center in Bedlewo, Poland in July 2005.
A Deep Q-Learning framework tackles market-making by incorporating closing auctions.
problem Managing end-of-day risk in market-making models.
method Developed a Deep Q-Learning framework that anticipates closing auctions and continuously refines projected clearing prices.
result The Deep Q-Learning framework outperforms classical market-making models in simulations and real data.
Study shows significant changes in trading volume and volatility patterns after 2008 financial crisis.
problem Non-stationary intraday statistical properties of trading volume and volatility.
method Analysis of blue chip equities trading volume and volatility over 2003-2014, split into semesters.
result Trading volume and volatility patterns changed significantly after 2008, with faster morning recovery and steeper afternoon.
CREIMBO models diverse brain activity by identifying hidden neural sub-circuits and their non-stationary interactions.
problem Lack of alignment in neural recordings limits analysis of brain-wide dynamics.
method CREIMBO learns a unified model of neural dynamics by assuming multiple hidden global sub-circuits representing ensemble interactions.
result CREIMBO discovers session-specific neural ensembles and their non-stationary interactions, revealing cross-subject neural mechanisms.
A minimal model of a market of myopic non-cooperative agents who trade bilaterally with random bids reproduces qualitative features of short-term electric power markets, such as those in California and New England. Each agent knows its own budget and preferences but not those of any other agent. The near-equilibrium pr…
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.
End-to-end speaker recognition method using neural networks.
problem Speaker and session variability in speaker verification.
method Joint Factor Analysis with tied hidden variables, MAP adaptation, two-step backpropagation.
result Improved likelihood ratios and robust performance on RSR2015 database.
SessionPath improves category suggestions in type-ahead search.
problem Improving precision and recall in eCommerce type-ahead suggestions.
method SessionPath uses session embeddings and a probability distribution model to predict facets.
result SessionPath outperforms count-based and neural models in eCommerce shops.
Unified product embeddings improve cross-task performance in e-commerce.
problem Training product embeddings in isolation limits cross-task performance.
method Combining text, clickstream, and image data using denoising auto-encoders, BPR, and Siamese neural networks.
result Unified product embeddings uniformly outperform isolated embeddings across three e-commerce tasks.