BPR: Bayesian Personalized Ranking from Implicit Feedback

Abstract

Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorization (MF) or adaptive knearest-neighbor (kNN). Even though these methods are designed for the item prediction task of personalized ranking, none of them is directly optimized for ranking. In this paper we present a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem. We also provide a generic learning algorithm for optimizing models with respect to BPR-Opt. The learning method is based on stochastic gradient descent with bootstrap sampling. We show how to apply our method to two state-of-the-art recommender models: matrix factorization and adaptive kNN. Our experiments indicate that for the task of personalized ranking our optimization method outperforms the standard learning techniques for MF and kNN. The results show the importance of optimizing models for the right criterion.

Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)

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• An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation
• Multi-Graph Convolution Collaborative Filtering
• Variance Reduction with Sparse Gradients
• Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation
• Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
• Graph Attention Collaborative Similarity Embedding for Recommender System
• Multi-Scale Quasi-RNN for Next Item Recommendation
• DREAM: A Dynamic Relational-Aware Model for Social Recommendation
• PEN4Rec: Preference Evolution Networks for Session-based Recommendation
• User Embedding based Neighborhood Aggregation Method for Inductive Recommendation
• EXTRA: Explanation Ranking Datasets for Explainable Recommendation
• It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation
• Self-Attentive Neural Collaborative Filtering
• CSSR: A Context-Aware Sequential Software Service Recommendation Model
• Recommender Systems Notation: Proposed Common Notation for Teaching and Research
• Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start
• Temporal Collaborative Ranking Via Personalized Transformer
• Multi-behavioral Sequential Prediction with Recurrent Log-bilinear Model
• Towards Amortized Ranking-Critical Training for Collaborative Filtering
• One Size Does Not Fit All: Modeling Users' Personal Curiosity in Recommender Systems
• Ranking Median Regression: Learning to Order through Local Consensus
• Probabilistic Ensemble of Collaborative Filters
• PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability
• Multi-Sample based Contrastive Loss for Top-k Recommendation
• Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems
• Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning
• Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction
• MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation
• A Personalized Subreddit Recommendation Engine
• Drug Package Recommendation via Interaction-aware Graph Induction
• Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report
• GEMRec: Towards Generative Model Recommendation
• Tripartite Vector Representations for Better Job Recommendation
• Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
• Explore User Neighborhood for Real-time E-commerce Recommendation
• Path-Based Reasoning over Heterogeneous Networks for Recommendation via Bidirectional Modeling
• Multi-Relational Learning at Scale with ADMM
• Fairness-aware Personalized Ranking Recommendation via Adversarial Learning
• Dual Side Deep Context-aware Modulation for Social Recommendation
• Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation
• Embedding Cardinality Constraints in Neural Link Predictors
• Interest-aware Message-Passing GCN for Recommendation
• PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity
• SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback
• Evaluating Recommender System Algorithms for Generating Local Music Playlists
• Switch Spaces: Learning Product Spaces with Sparse Gating
• Learning Robust Recommender from Noisy Implicit Feedback
• SamWalker++: recommendation with informative sampling strategy
• Tricolore: Multi-Behavior User Profiling for Enhanced Candidate Generation in Recommender Systems
• Computing recommendations via a Knowledge Graph-aware Autoencoder
• CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation
• A Neural Attention Model for Adaptive Learning of Social Friends' Preferences
• Top-N Recommendation with Counterfactual User Preference Simulation
• Automatic Representation for Lifetime Value Recommender Systems
• Group-Buying Recommendation for Social E-Commerce
• Implicit Feedback Deep Collaborative Filtering Product Recommendation System
• Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
• CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
• Keen2Act: Activity Recommendation in Online Social Collaborative Platforms
• Thresholding for Top-k Recommendation with Temporal Dynamics
• A Pre-processing Method for Fairness in Ranking
• Effective Visualization and Analysis of Recommender Systems
• Quaternion Collaborative Filtering for Recommendation
• Dynamic-K Recommendation with Personalized Decision Boundary
• Dynamic Graph Collaborative Filtering
• Enhancing Factorization Machines with Generalized Metric Learning
• LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text
• Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
• A Missing Information Loss function for implicit feedback datasets
• Scalable Bayesian Modelling of Paired Symbols
• A Neural Network Based Explainable Recommender System
• Textually Guided Ranking Network for Attentional Image Retweet Modeling
• Cyberswarm: a novel swarm intelligence algorithm inspired by cyber community dynamics
• MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation
• Learning to Embed Categorical Features without Embedding Tables for Recommendation
• Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering
• Generalized Embedding Machines for Recommender Systems
• Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-based Recommendation
• Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings
• Multi-Step Adversarial Perturbations on Recommender Systems Embeddings
• Exact-K Recommendation via Maximal Clique Optimization
• A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care
• Linear, or Non-Linear, That is the Question!
• MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning
• Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
• Learning Elastic Embeddings for Customizing On-Device Recommenders
• Improving One-class Recommendation with Multi-tasking on Various Preference Intensities
• Scientific Dataset Discovery via Topic-level Recommendation
• SVP-CF: Selection via Proxy for Collaborative Filtering Data
• How Powerful is Graph Convolution for Recommendation?
• A Unified Framework for Cross-Domain and Cross-System Recommendations
• Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems
• Problem Learning: Towards the Free Will of Machines
• Knowledge Graph-enhanced Sampling for Conversational Recommender System
• False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation
• Hierarchical User Intent Graph Network forMultimedia Recommendation
• Hierarchical Dynamic Modeling for Individualized Bayesian Forecasting
• Density-Ratio Based Personalised Ranking from Implicit Feedback
• Learning Fair Representations for Recommendation: A Graph-based Perspective
• SceneRec: Scene-Based Graph Neural Networks for Recommender Systems
• HSR: Hyperbolic Social Recommender
• DeepGroup: Representation Learning for Group Recommendation with Implicit Feedback
• Multi-Facet Recommender Networks with Spherical Optimization
• Collaborative Filtering Ensemble for Personalized Name Recommendation
• BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism
• On the instability of embeddings for recommender systems: the case of Matrix Factorization
• NeuSE: A Neural Snapshot Ensemble Method for Collaborative Filtering
• Variational Inference for Category Recommendation in E-Commerce platforms
• Practical Data Poisoning Attack against Next-Item Recommendation
• CSRN: Collaborative Sequential Recommendation Networks for News Retrieval
• Directional Multivariate Ranking
• MRIF: Multi-resolution Interest Fusion for Recommendation
• Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization
• Recommending Given Names
• Long-tail Session-based Recommendation
• The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking
• BPMR: Bayesian Probabilistic Multivariate Ranking
• Double-Wing Mixture of Experts for Streaming Recommendations
• Recommendation with Attribute-aware Product Networks: A Representation Learning Model
• Collaborative Filtering and Multi-Label Classification with Matrix Factorization
• SeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System
• Learning Item Trees for Probabilistic Modelling of Implicit Feedback
• An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders
• Overcoming Data Sparsity in Group Recommendation
• Conceptualize and Infer User Needs in E-commerce
• Neural Logic Networks
• One-class Collaborative Filtering with Random Graphs: Annotated Version
• Deep Item-based Collaborative Filtering for Top-N Recommendation
• Sequence-Aware Factorization Machines for Temporal Predictive Analytics
• Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
• Attribute-aware Collaborative Filtering: Survey and Classification
• Using Taste Groups for Collaborative Filtering
• Memory Augmented Graph Neural Networks for Sequential Recommendation
• Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping
• Advances in Collaborative Filtering and Ranking
• Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation
• NAIRS: A Neural Attentive Interpretable Recommendation System
• Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
• Hierarchical Context enabled Recurrent Neural Network for Recommendation
• Leveraging Trust and Distrust in Recommender Systems via Deep Learning
• Demystifying Core Ranking in Pinterest Image Search
• Representation Learning for Words and Entities
• Bandit Learning for Diversified Interactive Recommendation
• An Item Recommendation Approach by Fusing Images based on Neural Networks
• Effects of Foraging in Personalized Content-based Image Recommendation
• Row-less Universal Schema
• Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation
• printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning
• Multi-Label Learning with Provable Guarantee
• Online Evaluation Methods for the Causal Effect of Recommendations
• Learning Feature Interactions with Lorentzian Factorization Machine
• Decoupled Variational Embedding for Signed Directed Networks
• Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data
• Sequential Recommendation for Cold-start Users with Meta Transitional Learning
• Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
• Time-weighted Attentional Session-Aware Recommender System
• A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation
• Recommendation under Capacity Constraints
• Distributed Function Minimization in Apache Spark
• Consistency-Aware Recommendation for User-Generated ItemList Continuation
• Ranking metrics on non-shuffled traffic
• Visually Aware Skip-Gram for Image Based Recommendations
• Learning Consumer and Producer Embeddings for User-Generated Content Recommendation
• Differentially Private Neighborhood-based Recommender Systems
• DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain
• Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs
• Sequential Recommender via Time-aware Attentive Memory Network
• A Probabilistic View of Neighborhood-based Recommendation Methods
• Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
• Latent Relation Representations for Universal Schemas
• Utilizing FastText for Venue Recommendation
• Addressing Class-Imbalance Problem in Personalized Ranking
• Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation
• Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
• Personalized Bundle Recommendation in Online Games
• Neural Collaborative Ranking
• Attribute-aware Explainable Complementary Clothing Recommendation
• Collaborative Similarity Embedding for Recommender Systems
• Dual Adversarial Variational Embedding for Robust Recommendation
• Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
• Multi-Label Network Classification via Weighted Personalized Factorizations
• PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation
• Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering
• Freudian and Newtonian Recurrent Cell for Sequential Recommendation
• Location Prediction via Bi-direction Speculation and Dual-level Association
• A Behavior-aware Graph Convolution Network Model for Video Recommendation
• Spectrum-enhanced Pairwise Learning to Rank
• Learning Hierarchical Item Categories from Implicit Feedback Data for Efficient Recommendations and Browsing
• Dual-embedding based Neural Collaborative Filtering for Recommender Systems
• Compositional Coding for Collaborative Filtering
• Leveraging Review Properties for Effective Recommendation
• Causal Embeddings for Recommendation: An Extended Abstract
• UGRec: Modeling Directed and Undirected Relations for Recommendation
• Scalable Personalised Item Ranking through Parametric Density Estimation
• Bootstrapping User and Item Representations for One-Class Collaborative Filtering
• Causality-Aware Neighborhood Methods for Recommender Systems
• Rating and aspect-based opinion graph embeddings for explainable recommendations
• Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations
• Adaptively Weighted Top-N Recommendation for Organ Matching
• Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
• On the Effectiveness of Low-rank Approximations for Collaborative Filtering compared to Neural Networks
• CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation
• Collaborative Translational Metric Learning
• Coarse-to-Fine Lightweight Meta-Embedding for ID-Based Recommendation
• New Recommendation Algorithm for Implicit Data Motivated by the Multivariate Normal Distribution
• Coupled Variational Recurrent Collaborative Filtering
• Topology Distillation for Recommender System
• Dual Correction Strategy for Ranking Distillation in Top-N Recommender System
• TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations
• Deep Pairwise Hashing for Cold-start Recommendation
• Quaternion-Based Graph Convolution Network for Recommendation
• Adaptive Deep Learning of Cross-Domain Loss in Collaborative Filtering
• GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
• Efficient Data-specific Model Search for Collaborative Filtering
• Simple and Powerful Architecture for Inductive Recommendation Using Knowledge Graph Convolutions
• Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
• Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
• Breaking the Softmax Bottleneck for Sequential Recommender Systems with Dropout and Decoupling
• Neural Cross-Domain Collaborative Filtering with Shared Entities
• RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
• Binary Latent Representations for Efficient Ranking: Empirical Assessment
• Structured Recommendation
• PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion
• PAS: A Position-Aware Similarity Measurement for Sequential Recommendation
• On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics
• Mining Online Social Data for Detecting Social Network Mental Disorders
• Simulations for novel problems in recommendation: analyzing misinformation and data characteristics
• An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization
• Personalized Video Recommendation Using Rich Contents from Videos
• NU:BRIEF -- A Privacy-aware Newsletter Personalization Engine for Publishers
• Integrating Topic Models and Latent Factors for Recommendation
• Multi-Task Triplet Loss for Named Entity Recognition using Supplementary Text
• Retrieval & Interaction Machine for Tabular Data Prediction
• Predicting risky behavior in social communities
• Structural Analysis of User Choices for Mobile App Recommendation
• Comprehensive Personalized Ranking Using One-Bit Comparison Data
• A Deep Hybrid Model for Recommendation Systems
• Monotone Retargeting for Unsupervised Rank Aggregation with Object Features
• Personalized Multimedia Item and Key Frame Recommendation
• Stratified and Time-aware Sampling based Adaptive Ensemble Learning for Streaming Recommendations
• Visually-aware Recommendation with Aesthetic Features
• Fully Hyperbolic Graph Convolution Network for Recommendation
• ILCR: Item-based Latent Factors for Sparse Collaborative Retrieval
• A collaborative filtering model with heterogeneous neural networks for recommender systems
• Cross-Domain Collaborative Filtering via Translation-based Learning
• Personalized Neural Embeddings for Collaborative Filtering with Text
• The Apps You Use Bring The Blogs to Follow
• Cascading: Association Augmented Sequential Recommendation
• Hierarchical Bayesian Personalized Recommendation: A Case Study and Beyond
• Gated Attentive-Autoencoder for Content-Aware Recommendation
• LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models
• TRec: Sequential Recommender Based On Latent Item Trend Information
• Attentive Long Short-Term Preference Modeling for Personalized Product Search
• Sequential Variational Autoencoders for Collaborative Filtering
• Beyond Next Item Recommendation: Recommending and Evaluating List of Sequences
• Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality
• Learning Preferences and Demands in Visual Recommendation
• Beyond Similarity: Relation Embedding with Dual Attentions for Item-based Recommendation
• Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack
• Collaborative Filtering with Stability
• IteRank: An iterative network-oriented approach to neighbor-based collaborative ranking
• Fast Non-Bayesian Poisson Factorization for Implicit-Feedback Recommendations