Matrix Factorization Techniques for Recommender Systems

2009/08/01 by Yehuda Koren, Robert Bell, Chris Volinsky · 11,784 citations
Business, Management and Accounting · Computer Science · Mathematics · #Algorithm #Artificial intelligence #Computer science #Consumer Market Behavior and Pricing #Factorization #Image Retrieval and Classification Techniques #Information retrieval #Mathematics #Matrix (chemical analysis) #Matrix decomposition #Non-negative matrix factorization #Product (mathematics) #Recommender Systems and Techniques #Recommender system #Theoretical computer science

paper · doi:10.1109/mc.2009.263

published in Computer 42(8), 30-37 (IEEE Computer Society)

openalex publication_date 2009/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.

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• Meta-learning for Matrix Factorization without Shared Rows or Columns
• Compositional Coding for Collaborative Filtering
• Content Filtering Enriched GNN Framework for News Recommendation
• PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems
• User Profiling from Reviews for Accurate Time-Based Recommendations
• Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion
• A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care
• RecPS: Privacy Risk Scoring for Recommender Systems
• Matrix Completion Using Alternating Minimization for Distribution System State Estimation
• GAN-based Recommendation with Positive-Unlabeled Sampling
• Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective
• On-Device User Intent Prediction for Context and Sequence Aware Recommendation
• Context-aware Tree-based Deep Model for Recommender Systems
• A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems
• Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality
• Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks
• CoRGi: Content-Rich Graph Neural Networks with Attention
• Learning Fair Representations for Recommendation: A Graph-based Perspective
• DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
• Enhancing POI Recommendation through Global Graph Disentanglement with POI Weighted Module
• Recovering Metabolic Networks using A Novel Hyperlink Prediction Method
• Online Forecasting Matrix Factorization
• Using Linear Dynamical Topic Model for Inferring Temporal Social Correlation in Latent Space
• Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms
• Proportionate gradient updates with PercentDelta
• A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses
• Noisy intermediate-scale quantum (NISQ) algorithms
• Randomized LU decomposition
• Reinforcement Learning for Strategic Recommendations
• A Hybrid Variational Autoencoder for Collaborative Filtering
• "Beyond the past": Leveraging Audio and Human Memory for Sequential Music Recommendation
• R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
• Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems
• Predict your Click-out: Modeling User-Item Interactions and Session Actions in an Ensemble Learning Fashion
• Natural Language Processing via LDA Topic Model in Recommendation Systems
• A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
• SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
• Why Isn't Relational Learning Taking Over the World?
• Spring-Electrical Models For Link Prediction
• A collaborative filtering model with heterogeneous neural networks for recommender systems
• Low Rank Regularization: A Review
• Looking for Fairness in Recommender Systems
• Argumentation meets matrix factorization: A dual perspective for explainable recommendations
• Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems
• Attentive Geo-Social Group Recommendation
• Seq2seq Translation Model for Sequential Recommendation
• Music Information Retrieval: Recent Developments and Applications
• Can we leverage rating patterns from traditional users to enhance recommendations for children?
• Similar but Different: Exploiting Users' Congruity for Recommendation Systems
• Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation
• Convex Factorization Machine for Regression
• Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison
• New Fairness Metrics for Recommendation that Embrace Differences
• A Survey on Recommender Systems Using Graph Neural Network
• Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering
• Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
• Graph Factorization Machines for Cross-Domain Recommendation
• UGRec: Modeling Directed and Undirected Relations for Recommendation
• Improved Constructions for Secure Multi-Party Batch Matrix Multiplication
• Neural Logic Networks
• Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness
• Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search
• Fast Distributed Bandits for Online Recommendation Systems
• AutoRec: An Automated Recommender System
• Audience Creation for Consumables -- Simple and Scalable Precision Merchandising for a Growing Marketplace
• Word Representations via Gaussian Embedding
• RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems
• Flatter is better: Percentile Transformations for Recommender Systems
• Non-parametric Graph Convolution for Re-ranking in Recommendation Systems
• BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System
• Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method
• Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems
• TransRev: Modeling Reviews as Translations from Users to Items
• Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems
• Structured Recommendation
• Revisiting Graph Projections for Effective Complementary Product Recommendation
• A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms
• Crowdsourced Task Routing via Matrix Factorization
• GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
• Solving Cold-Start Problem in Large-scale Recommendation Engines: A Deep Learning Approach
• RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation
• KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation
• Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects
• Collaborative Competitive filtering II: Optimal Recommendation and Collaborative Games
• Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
• PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process
• Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations
• BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization
• Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction
• SCOPE: Scalable Composite Optimization for Learning on Spark
• A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks
• Two-Sided Fairness in Non-Personalised Recommendations
• Leveraging Schema Labels to Enhance Dataset Search
• The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
• Parallel Matrix Factorization for Binary Response
• Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation
• Differentiable Product Quantization for End-to-End Embedding Compression
• Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation
• An Item Recommendation Approach by Fusing Images based on Neural Networks
• Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping
• Self-Attentive Sequential Recommendation
• Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
• Structured Matrix Completion with Applications to Genomic Data Integration
• Generate Natural Language Explanations for Recommendation
• Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction
• Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values
• Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework
• Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data
• Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation
• Large-scale Interactive Recommendation with Tree-structured Policy Gradient
• Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
• Single-Layer Graph Convolutional Networks For Recommendation
• Compositions of Variant Experts for Integrating Short-Term and Long-Term Preferences
• Matrix Factorization Method for Decentralized Recommender Systems
• Extracting Features from Ratings: The Role of Factor Models
• Interact2Vec -- An efficient neural network-based model for simultaneously learning users and items embeddings in recommender systems
• On Fine-Grained Distinct Element Estimation
• Dual-embedding based Neural Collaborative Filtering for Recommender Systems
• Bilinear Generalized Vector Approximate Message Passing
• Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization
• High-dimensional Time Series Prediction with Missing Values
• Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems
• Echo Chambers in Collaborative Filtering Based Recommendation Systems
• Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization
• Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices
• RecLLM-R1: A Two-Stage Training Paradigm with Reinforcement Learning and Chain-of-Thought v1
• Decentralized Recommender Systems
• A Soft Recommender System for Social Networks
• Serverless Straggler Mitigation using Local Error-Correcting Codes
• Differential Data Analysis for Recommender Systems
• Machine Learning Based Student Grade Prediction: A Case Study
• Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems
• AdaptGOT: A Pre-trained Model for Adaptive Contextual POI Representation Learning
• Together or Alone: The Price of Privacy in Collaborative Learning
• Canonical Tensor Decomposition for Knowledge Base Completion
• Learning User Representations with Hypercuboids for Recommender Systems
• Recent Advances in Diversified Recommendation
• A Deep Reinforcement Learning Chatbot (Short Version)
• DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation
• Implicit Regularization in Matrix Sensing via Mirror Descent
• Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
• Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
• Distributed Data Vending on Blockchain
• All-at-once Optimization for Coupled Matrix and Tensor Factorizations
• STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
• Predicting next shopping stage using Google Analytics data for E-commerce applications
• Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
• Academic Performance Estimation with Attention-based Graph Convolutional Networks
• Line Graph Neural Networks for Link Prediction
• Coded Computing and Cooperative Transmission for Wireless Distributed Matrix Multiplication
• Learning a Latent Space of Multitrack Measures
• Would you Like to Talk about Sports Now? Towards Contextual Topic Suggestion for Open-Domain Conversational Agents
• Heterogeneous Information Network Embedding for Recommendation
• Coded Alternating Least Squares for Straggler Mitigation in Distributed Recommendations
• A²-GCN: An Attribute-aware Attentive GCN Model for Recommendation
• Discriminative Features via Generalized Eigenvectors
• A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions
• Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
• Personalized Next Point-of-Interest Recommendation via Latent Behavior Patterns Inference
• Interacting Attention-gated Recurrent Networks for Recommendation
• Tensor Decompositions for temporal knowledge base completion
• A Collaborative Process Parameter Recommender System for Fleets of Networked Manufacturing Machines -- with Application to 3D Printing
• A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication
• Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
• Course Project Report: Comparing MCMC and Variational Inference for Bayesian Probabilistic Matrix Factorization on the MovieLens Dataset
• Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)
• Conquering the rating bound problem in neighborhood-based collaborative filtering: a function recovery approach
• Low-rank Matrix Completion using Alternating Minimization
• NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation
• Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation
• Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation
• Infusing Collaborative Recommenders with Distributed Representations
• Point2Skeleton: Learning Skeletal Representations from Point Clouds
• The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News
• Simultaneous Relevance and Diversity: A New Recommendation Inference Approach
• Towards a More Reliable Privacy-preserving Recommender System
• Time-weighted Attentional Session-Aware Recommender System
• On Differential Privacy for Adaptively Solving Search Problems via Sketching
• WANDER: An Explainable Decision-Support Framework for HPC
• PathRec: Visual Analysis of Travel Route Recommendations
• Phonetic Word Embeddings
• Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up\n and Enhance Recommendations
• CryptoRec: Privacy-preserving Recommendation as a Service
• Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation
• MultiHead MultiModal Deep Interest Recommendation Network
• Collaborative Filtering and Multi-Label Classification with Matrix Factorization
• Cold-start recommendations in Collective Matrix Factorization
• Thresholding for Top-k Recommendation with Temporal Dynamics
• Many-to-one Recurrent Neural Network for Session-based Recommendation
• Hybrid Collaborative Filtering Models for Clinical Search Recommendation
• Convolutional Geometric Matrix Completion
• Conversational Recommender System
• Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
• KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems
• Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders
• Dynamic Collaborative Filtering with Compound Poisson Factorization
• Recurrent Neural Networks for Long and Short-Term Sequential Recommendation
• Alternating Energy Minimization Methods for Multi-term Matrix Equations
• ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
• Principled Evaluation with Human Labels: One Rater at a Time and Rater Equivalence
• ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding
• Dynamic Memory based Attention Network for Sequential Recommendation
• Explainable Recommendation: Theory and Applications
• N²: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
• A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System
• TestAgent: An Adaptive and Intelligent Expert for Human Assessment
• Combining social relations and interaction data in Recommender System with Graph Convolution Collaborative Filtering
• Learning Binarized Representations with Pseudo-positive Sample Enhancement for Efficient Graph Collaborative Filtering
• Learning Cost Functions for Optimal Transport
• Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
• Optimization of Epsilon-Greedy Exploration
• Recommendation from Raw Data with Adaptive Compound Poisson Factorization
• Skewness Ranking Optimization for Personalized Recommendation
• Local Optimality of User Choices and Collaborative Competitive Filtering
• GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation
• Dynamic Matrix Factorization: A State Space Approach
• A comparative study of similarity-based and GNN-based link prediction approaches
• DDTCDR: Deep Dual Transfer Cross Domain Recommendation
• Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization
• Cascading: Association Augmented Sequential Recommendation
• Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search
• Counterfactual Inference for Eliminating Sentiment Bias in Recommender Systems
• Diversity Regularized Interests Modeling for Recommender Systems
• Addressing Marketing Bias in Product Recommendations
• CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
• Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
• Graph Contrastive Learning for Optimizing Sparse Data in Recommender Systems with LightGCL
• Attribute-aware Collaborative Filtering: Survey and Classification
• Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model
• One Rank at a Time: Cascading Error Dynamics in Sequential Learning
• Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences
• Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
• The Technological Gap Between Virtual Assistants and Recommendation Systems
• AspeRa: Aspect-based Rating Prediction Model
• Counterfactual Multi-player Bandits for Explainable Recommendation Diversification
• Embed Progressive Implicit Preference in Unified Space for Deep Collaborative Filtering
• GGBond: Growing Graph-Based AI-Agent Society for Socially-Aware Recommender Simulation
• Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion
• GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
• Point of Interest Recommendation Methods in Location Based Social Networks: Traveling to a new geographical region
• AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
• Conditional Restricted Boltzmann Machines for Cold Start Recommendations
• Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation
• Understanding the Performance Gap in Preference Learning: A Dichotomy of RLHF and DPO
• Fast, Warped Graph Embedding: Unifying Framework and One-Click Algorithm
• 1-Bit Matrix Completion under Exact Low-Rank Constraint
• Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph
• Denoising User-aware Memory Network for Recommendation
• Pursuits in Structured Non-Convex Matrix Factorizations
• Fast Differentially Private Matrix Factorization
• Stochastic Bandits with Context Distributions
• Learning to Select Historical News Articles for Interaction based Neural News Recommendation
• Blind Compressive Sensing Framework for Collaborative Filtering
• On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics
• Pyramid: A General Framework for Distributed Similarity Search
• Joint Neural Collaborative Filtering for Recommender Systems
• Try This Instead: Personalized and Interpretable Substitute Recommendation
• Enhancing CTR Prediction with De-correlated Expert Networks
• Dual Side Deep Context-aware Modulation for Social Recommendation
• Disentangled Item Representation for Recommender Systems
• MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
• ColdGAN: Resolving Cold Start User Recommendation by using Generative Adversarial Networks
• NPTC-net: Narrow-Band Parallel Transport Convolutional Neural Network on Point Clouds
• Temporal Matrix Factorization for Tracking Concept Drift in Individual User Preferences
• MODE: Mutual Optimality in Direct Effects of Reciprocal Recommendations in Matching Markets
• Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems
• Reliable Deep Grade Prediction with Uncertainty Estimation
• A Novel Generative Model with Causality Constraint for Mitigating Biases in Recommender Systems
• Learning to Initialize Gradient Descent Using Gradient Descent
• Efficient Tensor Decomposition with Boolean Factors
• ThinkRec: Thinking-based recommendation via LLM
• A content-based recommendation approach based on singular value decomposition
• Low-Rank Modeling and Its Applications in Image Analysis
• Expert Finding in Community Question Answering: A Review
• Group-sparse Embeddings in Collective Matrix Factorization
• Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
• PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs
• Graph Algorithms for Multiparallel Word Alignment
• Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
• Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective
• GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation
• SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding
• Dynamic Matrix Factorization with Priors on Unknown Values
• Show Me the Money: Dynamic Recommendations for Revenue Maximization
• Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework
• Randomized Value Functions via Posterior State-Abstraction Sampling
• MLlib: Machine Learning in Apache Spark
• Adherence and Constancy in LIME-RS Explanations for Recommendation
• COMET: Convolutional Dimension Interaction for Collaborative Filtering
• BLC: Private Matrix Factorization Recommenders via Automatic Group Learning
• Extendable Neural Matrix Completion
• On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective
• A Local Polyak-Lojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models
• Embedding models for recommendation under contextual constraints
• Adaptive Deep Learning of Cross-Domain Loss in Collaborative Filtering
• Is Supervised Learning Really That Different from Unsupervised?
• Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
• Consistent Collective Matrix Completion under Joint Low Rank Structure
• Regularized Singular Value Decomposition and Application to Recommender System
• Grade Prediction with Temporal Course-wise Influence
• Dynamic Content Update for Wireless Edge Caching via Deep Reinforcement Learning
• Evaluating Recommender System Algorithms for Generating Local Music Playlists
• LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosis
• Adaptively-weighted Nearest Neighbors for Matrix Completion
• Context-aware short-term interest first model for session-based recommendation
• Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
• Affinity Weighted Embedding
• An Adaptive Similarity Measure to Tune Trust Influence in Memory-Based Collaborative Filtering
• A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion
• Multi-Component Graph Convolutional Collaborative Filtering
• Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems
• Deep Latent Factor Model for Collaborative Filtering
• Temporal Collaborative Ranking Via Personalized Transformer
• Distributed Collaborative Hashing and Its Applications in Ant Financial
• Structured Matrix Recovery via the Generalized Dantzig Selector
• Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading
• Attention-based Mixture Density Recurrent Networks for History-based Recommendation
• Active and Adaptive Sequential learning
• Why are Big Data Matrices Approximately Low Rank?
• Joint Neural Collaborative Filtering for Recommender Systems
• Deep recommender engine based on efficient product embeddings neural pipeline
• Beyond Co-Occurrence: Multi-Modal Session-Based Recommendation
• Human Computation
• Negative Binomial Matrix Factorization for Recommender Systems
• Coupled Matrix Factorization within Non-IID Context
• Debiased Estimators in High-Dimensional Regression: A Review and Replication of Javanmard and Montanari (2014)
• From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
• Sublinear Maximum Inner Product Search using Concomitants of Extreme Order Statistics
• Understanding and Improving Proximity Graph based Maximum Inner Product Search
• Joint Text Embedding for Personalized Content-based Recommendation
• Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data
• On the instability of embeddings for recommender systems: the case of Matrix Factorization
• Multi-agents based User Values Mining for Recommendation
• Measuring the diversity of recommendations: a preference-aware approach for evaluating and adjusting diversity
• Hybrid Recommender System Based on Personal Behavior Mining
• Latent Multi-Criteria Ratings for Recommendations
• Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation
• JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation
• Scalable Realistic Recommendation Datasets through Fractal Expansions
• A Constrained Matrix-Variate Gaussian Process for Transposable Data
• Tensor Completion Algorithms in Big Data Analytics
• Link Prediction Based on Graph Neural Networks
• Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
• Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
• Neural Attentive Session-based Recommendation
• STAMP
• Visually-aware Recommendation with Aesthetic Features
• Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
• Learning Fairness-aware Relational Structures
• War of Words II: Enriched Models of Law-Making Processes
• Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
• Learning Elastic Embeddings for Customizing On-Device Recommenders
• Privacy Threats Against Federated Matrix Factorization
• Representation Learning for cold-start recommendation
• Incorporating Chinese Characters of Words for Lexical Sememe Prediction
• Graph Attention Collaborative Similarity Embedding for Recommender System
• Latent Contextual Bandits and their Application to Personalized Recommendations for New Users
• Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative\n Filtering
• All together now: A perspective on the NETFLIX PRIZE
• Statistical Significance of the Netflix Challenge
• Eigen for Statistical and Machine Learning Computing: A Lightweight C++ Tutorial with Python Bindings
• Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat
• Private traits and attributes are predictable from digital records of human behavior
• Algorithm Performance Spaces for Strategic Dataset Selection
• Low-Rank Adaptation Redux for Large Models
• Completing a joint PMF from projections: a low-rank coupled tensor factorization approach
• Collectively Embedding Multi-Relational Data for Predicting User Preferences
• Gradual Cognitive Externalization: From Modeling Cognition to Constituting It
• Identifying Users From Their Rating Patterns
• Let's get together
• Beyond Predicting Responses: Conformal Inference for Latent Distributional Parameters
• On Universal Features for High-Dimensional Learning and Inference
• Cultivating Online: Question Routing in a Question and Answering Community for Agriculture
• Sketching Transformed Matrices with Applications to Natural Language Processing
• Who ordered this?: Exploiting implicit user tag order preferences for personalized image tagging
• On the Convergence of Alternating Direction Lagrangian Methods for\n Nonconvex Structured Optimization Problems
• Properties of sports ranking methods
• A Personalized Preference Learning Framework for Caching in Mobile Networks
• STAN: Spatio-Temporal Attention Network for Next Location Recommendation
• A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates
• Compact representation of temporal processes in echosounder time series via matrix decomposition
• Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality
• Applications of Social Media in Hydroinformatics: A Survey
• Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference
• Neural Graph Collaborative Filtering
• Convolutional Collaborative Filter Network for Video Based Recommendation Systems
• Representation Learning for Natural Language Processing
• Explanation as a Defense of Recommendation
• Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model
• Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback
• A Matrix Factorization Model for Hellinger-Based Trust Management in Social Internet of Things
• Where do goals come from? A Generic Approach to Autonomous Goal-System Development
• Dynamic Time-aware Continual User Representation Learning
• Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding
• Disentangling Long and Short-Term Interests for Recommendation
• Personalized Prediction of Vehicle Energy Consumption Based on Participatory Sensing
• AdaptRec: A Self-Adaptive Framework for Sequential Recommendations with Large Language Models
• Neural Rating Regression with Abstractive Tips Generation for Recommendation
• Personalized Embedding-based e-Commerce Recommendations at eBay
• Matrix Factorization on GPUs with Memory Optimization and Approximate Computing
• Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
• Scalability and sparsity issues in recommender datasets: a survey
• Structural Analysis of User Choices for Mobile App Recommendation
• Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving
• On Sampling Strategies for Neural Network-based Collaborative Filtering
• Matrix Factorization with Dynamic Multi-view Clustering for Recommender System
• Attention-based neural re-ranking approach for next city in trip recommendations
• GEMRank: Global Entity Embedding For Collaborative Filtering
• LoRe: Personalizing LLMs via Low-Rank Reward Modeling
• Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
• FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation
• StackRec
• Sequential Scenario-Specific Meta Learner for Online Recommendation
• Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
• Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability
• Global Optimality in Distributed Low-rank Matrix Factorization
• Session-Based Recommendation with Graph Neural Networks
• Deep learning methods for solving linear inverse problems: Research directions and paradigms
• LA-CTR: A Limited Attention Collaborative Topic Regression for Social Media
• Fairness-Aware Recommendation of Information Curators
• Toward Fair Recommendation in Two-sided Platforms
• A Greedy Approach for Budgeted Maximum Inner Product Search
• Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation
• Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
• How does the User's Knowledge of the Recommender Influence their\n Behavior?
• Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs
• On the Dimensionality of Embeddings for Sparse Features and Data
• DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
• SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation
• Is Personalized Modality Weighting Actually Personalized? A Controlled Audit of Per-User Weighting Claims in Multimodal Recommenders
• Predicting Task Difficulty Without Rollouts
• FlowAdam: Implicit Regularization via Geometry-Aware Soft Momentum Injection
• Exploring Backdoor Attack and Defense for LLM-empowered Recommendations
• Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation
• Structuring Wikipedia Articles with Section Recommendations
• Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
• Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning
• HistLLM: A Unified Framework for LLM-Based Multimodal Recommendation with User History Encoding and Compression
• Enhancing LLM-based Recommendation through Semantic-Aligned Collaborative Knowledge
• A Comparative Study of Recommender Systems under Big Data Constraints
• Explicit Uncertainty Modeling for Video Watch Time Prediction
• xDeepFM
• Temporal Dynamic Embedding for Irregularly Sampled Time Series
• Stratified Expert Cloning for Retention-Aware Recommendation at Scale
• Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation
• Heuristic Methods are Good Teachers to Distill MLPs for Graph Link Prediction
• Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems
• Hard Negative Sampling via Large Language Models for Recommendation
• Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
• Methods for biological data integration: perspectives and challenges. [europepmc]
• SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. [europepmc]
• Probability-based collaborative filtering model for predicting gene-disease associations. [europepmc]
• PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition. [europepmc]
• Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform. [europepmc]
• AutoImpute: Autoencoder based imputation of single-cell RNA-seq data. [europepmc]
• Multivariate Information Fusion With Fast Kernel Learning to Kernel Ridge Regression in Predicting LncRNA-Protein Interactions. [europepmc]
• McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data. [europepmc]
• A Novel Computational Model for Predicting microRNA-Disease Associations Based on Heterogeneous Graph Convolutional Networks. [europepmc]
• Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization. [europepmc]
• Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis. [europepmc]
• Prediction of circRNA-disease associations based on inductive matrix completion. [europepmc]
• Matrix factorization with neural network for predicting circRNA-RBP interactions. [europepmc]
• iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding. [europepmc]
• Evolution and impact of bias in human and machine learning algorithm interaction. [europepmc]
• News recommender system: a review of recent progress, challenges, and opportunities. [europepmc]
• A Benchmark for Data Imputation Methods. [europepmc]
• Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines. [europepmc]
• Artificial intelligence in E-Commerce: a bibliometric study and literature review. [europepmc]
• Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study. [europepmc]
• Graph Representation Learning and Its Applications: A Survey. [europepmc]
• Advancing Computational Toxicology by Interpretable Machine Learning. [europepmc]
• Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP. [europepmc]
• Computational drug repositioning with attention walking. [europepmc]
• Development of an Artificial Intelligence-Based Tailored Mobile Intervention for Nurse Burnout: Single-Arm Trial. [europepmc]

Related

• Feature-Based Matrix Factorization
• Clustering-Based Matrix Factorization
• Analysis and Visualization of the Parameter Space of Matrix Factorization-based Recommender Systems
• Dynamic recommender system : using cluster-based biases to improve the accuracy of the predictions
• An Introduction to Matrix factorization and Factorization Machines in Recommendation System, and Beyond