Improved Top-N recommender system using matrix completion.
problem Low-quality Top-N recommendations.
method Low-rank matrix completion with nonconvex rank relaxation and efficient optimization.
result Significantly improved Top-N recommendation accuracy.
Improved Top-N recommendations with novel rank approximation.
problem Low accuracy in recommender systems.
method Linear sparse and low-rank representation with nuclear norm relaxation.
result Significantly improved Top-N recommendation accuracy.
Top-N-Rank improves top N item recommendations in scalable recommender systems.
problem Improving top N item recommendations in scalable recommender systems.
method Proposes a novel list-wise Learning-to-Rank model optimizing a variant of DCG objective function, incorporating weights for implicit feedback.
result Significant improvement in ranking quality for top N recommendations.
A new KD model for collaborative filtering improves top-N recommendation performance.
problem Challenges in applying KD to recommender models due to feedback sparsity and ambiguity.
method Proposes a new KD model (CD) for collaborative filtering, reformulating a loss function, using probabilistic rank-aware sampling, and developing training strategies.
result Outperforms state-of-the-art methods by 2.7-33.2% in hit rate (HR) and 2.7-29.1% in NDCG.
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.
Hyperbolic geometry autoencoder outperforms Euclidean in top-N recommendation tasks.
problem Top-N recommendation performance using hyperbolic geometry.
method Simple autoencoder based on hyperbolic geometry with a single hidden layer.
result Outperforms Euclidean models and state-of-the-art methods.
Expressive recommender models deliver accurate top-N recommendations.
problem Improving recommendation accuracy while maintaining interpretability.
method Normalized nonnegative models that assign probability distributions to users and items.
result Performance matches PureSVD, providing interpretable user and item representations.
Develops LLFR for efficient top-N recommendation in big data.
problem Efficiently producing personalized recommendations in big data.
method Lanczos-based low-dimensional item similarity model for top-N recommendation.
result LLFR outperforms state-of-the-art methods in computational and qualitative aspects.
This study improves fast non-Bayesian Poisson factorization for implicit-feedback recommendation systems.
problem Improving recommendation quality and speed for implicit-feedback data.
method Regularized Poisson models, frequentist optimization, sparse solutions.
result Frequentist approach yields better top-N recommendations with shorter fitting times.
Data poisoning attacks can manipulate recommender systems to recommend target items.
problem Attacks on recommender systems to influence top-N item recommendations.
method Formulated as an optimization problem, solved using influence function to select influential users.
result Effective data poisoning attacks that outperform existing methods.
FPL allows users to control their data in federated top-N recommendation.
problem Data privacy in recommendation services.
method Federated Learning with learning to rank optimization.
result Users can control the amount of sensitive data shared with a central server.
Proposes a method to improve recommendation systems by considering negative feedback.
problem Insensitivity to negative feedback in top-N recommendation tasks.
method Model user feedback as a ternary variable and use third-order tensor factorization.
result Achieves state-of-the-art quality and significantly outperforms other methods in cold-start scenarios.
FBSM improves item recommendation for cold-start users by modeling feature interactions.
problem Cold-start item recommendation for new users.
method Factorized bilinear similarity model learning interactions among item features.
result Improves TOP-n recommendation performance compared to traditional methods.
New method improves stability of collaborative filtering.
problem Stability issues in matrix approximation for recommender systems.
method Introduces new optimization objectives and solves the optimization problem for stable matrix approximation.
result Achieves better accuracy in rating prediction and top-N recommendation tasks.
New methods improve deep learning models for sparse, high-dimensional data.
problem Underfitting in inference networks for sparse, high-dimensional data.
method Iterative optimization inspired by stochastic variational inference and improvements in sparse data representation.
result State-of-the-art results on text-count dataset and excellent recommendation results.
New method generates explainable recommendations from RBM-based CF.
problem Inaccurate and unexplainable recommender systems.
method Proposes an Explainable RBM technique for collaborative filtering.
result Effective in generating accurate and explainable recommendations.
The paper tackles cold-start recommendation problems in sparse item spaces.
problem Cold-start recommendation challenges in sparse item spaces.
method Exploits the hierarchical structure of the item space following the Decomposability theory.
result NCDREC outperforms state-of-the-art algorithms in recommendation accuracy, diversity, and sparseness insensitivity.
The study proposes an audit to assess user control over recommendations in collaborative filtering systems.
problem The gap between maximizing accuracy and ensuring user control over information availability in recommender systems.
method The approach involves a computationally efficient audit for top-N linear recommender models, focusing on reachability and user agency. result The study demonstrates that model complexity affects the effort required for users to exert control over their recommendations.
Unweighted matrix factorization can match or outperform weighted methods in recommender systems.
problem Improving recommendation performance with matrix factorization on implicit feedback data.
method Systematic study of various weighting schemes and matrix factorization algorithms.
result Training with unweighted data can perform comparably to, and sometimes outperform, training with weighted data.
Extends homotopical theory to locally compact groups, refining their compactness properties.
problem Developing homotopical invariants for locally compact groups.
method Extending classical theory of homotopical Σ-sets to locally compact Hausdorff groups, defining Σtopn sets of characters. result Recovering and generalizing classical results on characters and compactness properties of groups.
TransCF improves recommendation by modeling user-item relationships with translation vectors.
problem Triangle inequality violation in matrix factorization-based recommendation methods.
method TransCF uses translation vectors to model latent user-item relationships in implicit feedback.
result TransCF outperforms state-of-the-art methods by up to 17% in hit ratio.
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.
Reservoir subspace injection improves online ICA by preserving injected features.
problem Discarding injected features in top-n whitening can degrade performance. method Formalized reservoir subspace injection (RSI) and developed diagnostics (IER, SSO, ρ_x) to identify and mitigate the failure mode.
result RSI controller preserves passthrough retention, improving performance by up to 2.2 dB.
New method optimizes collaborative filtering for better ranking metrics.
problem Improving recommendation quality metrics like top-N ranking.
method Actor-critic reinforcement learning to directly optimize ranking metrics.
result The method outperforms state-of-the-art baselines on real-world datasets.
A new method for generating sets and graphs without requiring exchangeability.
problem Generating exchangeable distributions for sets and graphs is challenging.
method Top-n creation, a differentiable generation mechanism that selects relevant points from a latent vector.
result Top-n method outperforms i.i.d. generation in various tasks.
The smoothing theory is revised to generalize to different disc embedding spaces.
problem Generalizing the smoothing theory to various disc embedding spaces.
method Revising the Morlet-Burghelea-Lashof-Kirby-Siebenmann theorem to apply to different versions of disc smooth embedding spaces.
result The delooping of disc embedding spaces is shown to be compatible with the Hatcher and Budney actions.
A new portfolio model DEWSP improves Sharpe ratio by 0.24% to 5.15%.
problem High sensitivity of optimized portfolios to estimation errors.
method Deep learning algorithms predict returns for top-N ranked assets, then equally weight them.
result DEWSPs provide an improvement rate of 0.24% to 5.15% in terms of monthly Sharpe ratio compared to HEWSPs.
Improved QA system by re-ranking top-10 results using machine learning.
problem Enhance deployed QA systems without re-training.
method Learn similarity function using n-gram features; implement neural sentence embeddings.
result Mean reciprocal rank improves by 9.15%.
KANEL combines models for early hit enrichment in virtual screening.
problem Assessing model accuracy in chemical bioactivity predictions.
method Ensemble workflow using Kolmogorov-Arnold Networks (KANs) and other models.
result Improves early hit enrichment metrics like PPV@N.
Paper tackles zero-shot activity recognition using video features and text embeddings.
problem Zero-shot activity recognition with videos.
method Auto-encoder model for multimodal joint embedding, 3D convolutional action recognition for visual features, GloVe word embeddings for textual features.
result Improved zero-shot recognition results with top-n accuracy and mean Nearest Neighbor Overlap.
Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.
problem Recommender systems learn from user choices but can stall if users blindly follow recommendations.
method The recommender learns user knowledge by observing choices, abstaining from recommending a choice when multiple alternatives produce similar payoffs.
result Learning rate and social welfare improve when the recommender abstains from recommending certain choices.
CAFL breaks feedback loops in recommender systems using causal inference.
problem Feedback loops in recommender systems compromise recommendation quality and homogenize user behavior.
method Causal Adjustment for Feedback Loops (CAFL) algorithm that breaks feedback loops using causal inference.
result CAFL improves recommendation quality compared to prior correction methods.
Proposes a new model for diversified interactive recommendation.
problem Previous methods focus on optimizing recommendation accuracy, ignoring diversity.
method DC2B model using determinantal point process and Thompson sampling-based variational Bayesian inference. result Demonstrates effectiveness of the proposed method through extensive experiments.
Poisoning attacks improve graph-based recommender system recommendations.
problem Designing effective poisoning attacks for graph-based recommender systems.
method Formulated as an optimization problem, solved with techniques to assign rating scores to fake users.
result Outperforms existing attacks for graph-based recommender systems, recommending target items to 580 times more normal users.
Paper introduces a new job recommendation method using candidate job selection progression.
problem Traditional job recommendation methods are either filter-based or feature-based, limiting serendipitous and cold-start recommendations.
method Uses machine learning to analyze candidate job selection progression and derive latent competencies.
result Achieved best click-through rate in a real-world job recommender system.
Machine learning biases in job recommendations can lead to unfair outcomes.
problem Biased recommendations from recommender systems in job matching.
method Addressing biases at various stages of recommender systems training and deployment.
result Techniques can reduce bias in job recommendations, ensuring fair outcomes.
A new recommender system learns from user interactions to improve recommendations.
problem Mitigating information overload by personalizing item suggestions.
method Modeling interactions as MDP, using RL to learn optimal strategies, incorporating list-wise recommendations.
result The proposed framework LIRD improves recommendation effectiveness.
Paper proposes a hybrid system to address bias in recommender systems.
problem Bias in recommender systems exacerbates existing societal inequalities.
method Hybrid approach combining multiple similarity measures, content, and demographic info.
result Our model provides more accurate and fairer recommendations.
Extends recommender methods to respect capacity constraints.
problem Recommendation under capacity constraints in various settings.
method Extend three state-of-the-art latent factor recommendation approaches (PMF, GeoMF, BPR) to optimize for both recommendation accuracy and expected item usage that respects capacity constraints.
result Experimental results highlight the benefit of the method for recommendation under capacity constraints.
DeepFair improves fairness in recommender systems without sacrificing accuracy.
problem Lack of bias management in recommender systems leads to unfair recommendations for minority groups.
method Deep Learning based Collaborative Filtering algorithm that balances fairness and accuracy.
result It is possible to make fair recommendations without losing significant accuracy.
The paper aims to define a benchmark for deep learning recommendation models.
problem Insufficient benchmarking for deep learning recommendation models.
method Synthesizes modeling strategies, defines desirable characteristics, and summarizes advice from the MLPerf Recommendation Advisory Board.
result Defines an industry-relevant benchmark for deep learning recommendation models.
Interprets feature interactions in ad-click prediction models.
problem Improving interpretability of black-box recommender systems.
method Interprets feature interactions from a source model and encodes them in a target model.
result Interpretations significantly outperform existing recommender models.
New algorithm uses negative user preferences to improve recommendation quality.
problem Insufficient utilization of negative user preferences in recommender systems.
method Explicitly models negative user preferences to recommend more positive items.
result Improves recommendation quality by increasing accuracy and reducing negative items.
New cold-start recommendation method for recommender systems.
problem Making accurate recommendations for users and items with limited data.
method Proposes a faster cold-start prediction formula in collective matrix factorization models.
result Improved cold-start recommendations with reliable predictions for new users, better than non-personalized recommendations.
Survey on using knowledge graphs for better recommender systems.
problem Data sparsity and cold start issues in recommender systems.
method Utilizes knowledge graphs to improve recommendation accuracy and provide explanations.
result Advantages of knowledge graph-based recommender systems.
Advances citation and subject label recommendation using multi-modal adversarial autoencoders.
problem Improving recommendation systems for citations and subject labels.
method Multi-modal adversarial autoencoders with adversarial regularization, sparsity, and input modality analysis.
result Adversarial regularization consistently improves recommendation performance.
Proposes CF-SFL to improve sparse data recommendation.
problem Poor performance of CF in sparse data.
method Generative user feedback loop to simulate user feedback.
result Improves recommendation results on multiple datasets.
The paper improves recommendation models by considering user interactions with recommended items.
problem Improving next item prediction in recommendation systems.
method Extending RNN framework with a recommendation action module and state-action fusion module.
result Improved performance on next item prediction compared to baselines.