The paper shows how ignoring temporal context in recommender systems evaluation leads to false confidence, proposing a method to embed temporal context.
problem The discrepancy between offline and online recommender system performance evaluation.
method Proposes a training procedure to embed temporal context into recommender systems and validates its advantage using multi-objective optimization.
result Including temporal context in recommender systems evaluation can improve recall@20 by up to 20%.
Recommender system improves with temporal representations.
problem Improving interpretability and performance in recommender systems.
method Incorporates temporal representations via recurrent point process in continuous time.
result Characterizes effects of perception, interest, and seasonal changes on reviews.
SANST uses self-attentive networks with spatial and temporal embeddings for better POI recommendations.
problem Next point-of-interest (POI) recommendation for users based on their history.
method SANST incorporates spatio-temporal patterns into self-attentive networks.
result SANST outperforms state-of-the-art models by up to 13.65% in nDCG@10.
Neural M3 model adapts to diverse user behaviors over short and long timeframes.
problem Adapting to diverse user behaviors over short and long timeframes.
method Neural Multi-temporal-range Mixture Model (M3) combining short-term and long-term models with a learned gating mechanism.
result M3 consistently outperforms state-of-the-art sequential recommendation methods.
New model recommends stocks considering individual preferences and diversification.
problem Inaccurate stock price predictions and ignoring investment theories.
method Portfolio Temporal Graph Network Recommender (PfoTGNRec) incorporating diversification-enhancing sampling.
result PfoTGNRec outperforms state-of-the-art models in real-world data.
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.
MRIF models dynamic user interests at multiple temporal-ranges.
problem Capturing dynamic and multi-resolution user interests in recommendation.
method Multi-resolution Interest Fusion (MRIF) model that considers both temporal-ranges and drifts in user interests.
result MRIF outperforms state-of-the-art recommendation methods consistently.
A model for POI recommendation using relation embedding.
problem Challenges in POI recommendation due to sparse user-POI matrix and varying context.
method Translation-based relation embedding using Knowledge Graph Embedding techniques, combined matrix factorization framework.
result Demonstrates effectiveness of the proposed model on real-world datasets.
MEANTIME improves sequential recommendation by using multi-temporal embeddings and attention mechanisms.
problem Limited use of timestamp information and information bottleneck in sequential recommendation models.
method MEANTIME employs multiple types of temporal embeddings and attention mechanisms to capture diverse patterns from user behavior sequences.
result MEANTIME outperforms state-of-the-art sequential recommendation methods.
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical obs…
A framework combines multiple types of data for better item recommendations.
problem Limited performance of top-N recommendation systems using only one or two types of information.
method Design and implement GraFC2T2, a graph-based framework that encodes and combines content, temporal, and trust information.
result Combining different types of information improves recommendation performance.
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.
BoostJet combines statistical aggregates and neural embeddings for better recommendations.
problem Combining diverse user and offer features for improved recommendation quality.
method Integrates statistical aggregates and neural embeddings using MatrixNet.
result Significantly improved recommendation quality on Yandex's dataset.
This work tackles slate-based recommender systems using RL, optimizing long-term user engagement.
problem Optimizing long-term user engagement in slate-based recommender systems.
method Developed SLATEQ, a decomposition of RL methods for slate-based recommendations, and outlined a practical methodology.
result SLATEQ decomposes long-term value of a slate into component item-wise long-term values under mild assumptions.
Proposes TEMN for better POI recommendations.
problem Challenges in capturing user preferences and spatio-temporal POI relationships.
method Integrates topic model and memory network, incorporating geographical module.
result Improves POI recommendation effectiveness by 3.25% and 29.95%.
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.
RPF improves recommendation by modeling user and item interactions over time.
problem Temporal behavior and recurrent activities of users are not well modeled in existing recommendation systems.
method Introduces Recurrent Poisson Factorization (RPF) that uses a Poisson process to model temporal feedback.
result RPF outperforms state-of-the-art methods on various datasets.
Deep learning predicts M&A events in industry networks.
problem Predicting M&A behaviors in competitive industries with complex interdependencies.
method Temporal Dynamic Industry Network (TDIN) model using temporal point processes and deep learning.
result Effective M&A event prediction and actionable recommendations.
Proposes CVRCF for streaming recommender systems combining deep learning and probabilistic models.
problem Streaming recommendation problem with dynamic data and complexity.
method Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework integrating stochastic processes and deep factorization models.
result Favorable performance in temporal dependency modeling and predictive accuracy compared to state-of-the-art methods.
The paper presents a dataset and evaluates context-aware TV content recommendations.
problem Lack of data supporting context-aware TV content recommendation systems.
method Developed a dataset of TV consumption with contextual information and evaluated prediction performance.
result Including contextual features improves prediction accuracy, with social and temporal context contributing significantly.
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.
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.
New neural network predicts user-item relationships in evolving graphs.
problem Link prediction in dynamic graphs for recommendation services.
method Proposes a new neural network approach to leverage temporal contextual information.
result Our approach produces better predictions in scenarios with changing user-item relationships.
Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.
problem Relapse prevention for alcohol and tobacco addiction users.
method Records user profiles, tracks relapse history, uses machine learning for prediction, and recommends activities.
result Predictive machine learning algorithms help in preventing relapse.
Develops a real-time exercise recommendation system using deep learning.
problem Improving accuracy in exercise recommendation systems without user feedback.
method Deep recurrent neural network with attention mechanisms, real-time expert feedback.
result Improved accuracy in exercise recommendation system after real-time active learning.
The paper analyzes feedback loops in recommender systems causing echo chambers and filter bubbles.
problem Feedback loops in recommender systems leading to echo chambers and filter bubbles.
method Theoretical analysis of user dynamics and recommender system behavior.
result Solutions to slow down system degeneracy and understanding echo chambers and filter bubbles.
Hybrid system matches patients with family doctors based on trust and history.
problem Matching patients with suitable family doctors in primary care.
method Hybrid recommender system combining patient trust from consultation histories and temporal dynamics.
result Predictive accuracy is higher than heuristic and collaborative filtering approaches, and trust measure improves performance.
SeER hybrid model improves song recommendations and explains them.
problem Improving song recommendations and explaining them.
method Collaborative filtering and deep learning sequence models on MIDI content.
result Personalized explanations capture user preferences.
Enhances LightGCN for credit bond recommendations with dynamic node embeddings.
problem Challenges in static embeddings for rapidly evolving user interests in finance.
method Causal graph convolution for dynamic node embeddings over chronological user-item interactions.
result Significantly enhances LightGCN performance in financial product recommendations.
A new algorithm adapts to changing user behaviors in finance.
problem Adapting to changing user behaviors in financial recommendations.
method History-Augmented Collaborative Filtering using a custom neural network.
result The algorithm provides dynamic financial recommendations.
Personalized Transformer improves temporal collaborative ranking performance.
problem Temporal collaborative ranking in recommendation systems.
method Personalized Transformer model using attention mechanisms.
result Personalized Transformer outperforms SASRec by almost 5% in NDCG@10.
Proposes grade-aware course recommendation methods to improve student GPA.
problem Helping students select courses that lead to timely graduation and good grades.
method Two approaches: ranking courses by expected GPA impact and combining grade predictions with course recommendations.
result Grade-aware methods recommend courses leading to better student performance.
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.
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.
Improved item recommendations for repeat interactions using sequence analysis.
problem Limited effectiveness of traditional recommender systems in handling repeated user-item interactions.
method Designed a recommender system that considers sequences of item interactions for each user.
result Empirically shown to give highly accurate predictions and increase sales by 5%.
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.
A new tensor-based method for predicting temporal relationships in knowledge bases.
problem Predicting temporal relationships in evolving knowledge bases.
method Tensor decomposition of order 4 with new regularization schemes.
result Achieves state-of-the-art performance in temporal link prediction.
DVE models dynamic changes in feature embeddings for better sequence-aware applications.
problem Dynamic characterization of feature variations in time-varying data.
method Dynamic Variational Embedding (DVE) using recurrent neural networks.
result DVE models intrinsic nature and temporal variation of nodes effectively.
The paper evaluates classification and outlier detection algorithms for temporal data.
problem Improving accuracy in classification and outlier detection for temporal data.
method Comparison of six fast algorithms on various time-series datasets.
result Gradient Boosting Machines are best for classification, but no single algorithm is best for outlier detection.
Develops framework for understanding deep learning in time series data.
problem Understanding and explaining decisions made by deep learning models in time series data.
method Uses deep neural networks to capture and explain temporal dependencies in time series data.
result Framework successfully captures and explains temporal dependencies in various synthetic and real-world datasets.
THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.
problem Improving accuracy in predicting day-ahead electricity prices.
method Temporal hierarchy forecasting (THieF) reconciling hourly and block forecasts.
result THieF significantly improves accuracy (up to 13%) at all levels of prediction.
Superposed Hawkes processes improve risk bounds and solve cold-start issues.
problem Improving risk bounds in temporal point processes.
method Least squares estimation of superposed Hawkes processes.
result Superposed Hawkes processes tighten risk bounds under certain conditions.
Improves item recommendations by considering user experience evolution.
problem Current recommender systems ignore user experience evolution.
method Developed a generative HMM-LDA model to trace user evolution and interest facets.
result Significantly improved rating prediction over state-of-the-art baselines.
Advantage amplification helps RL in slow-evolving latent-state environments.
problem Challenges in reinforcement learning for long-horizon latent-state environments.
method Temporal abstraction and aggregation methods to overcome belief state error and small action advantage.
result Proven advantage amplification in settings with slowly evolving latent states.
Improves audio source separation using dilated convolutions and dense connections.
problem Optimizing feature extraction in audio source separation models.
method Adaptive dilated convolutions and dense connections in U-Net architecture.
result Improved performance on MUSDB test dataset.
Dynamic model captures spatial, temporal, and spatiotemporal volatility effects.
problem Analyzing volatility in spatial and temporal networks.
method Dynamic spatiotemporal and network ARCH model with common factors, Bayesian estimation.
result Model captures strong spatial/network interactions and spillover effects.
Introduces CuFun model for more accurate TPPs using CDF.
problem Challenges in forecasting future events in TPPs.
method Uses Cumulative Distribution Function (CDF) and monotonic neural network.
result Significantly improves adaptability and precision in TPPs.
This study evaluates and compares novelty detection algorithms for discrete sequences.
problem Identifying anomalies in temporal data.
method Experimental comparison of state-of-the-art novelty detection methods on various public and industrial datasets.
result Recommendations for efficient and appropriate methods based on extensive experiments and scalability tests.