Awesome Machine Learning (Statistics)

Differentially private matrix factorization with sub-linear convergence rate for personalized recommendation (2026)

Wang Zhou et al.

5.52

Deep recommendation algorithm based on reviews and descriptions neural matrix factorization under cold-start (2026)

Kechao Li et al.

4.20

ProReco: A Process Discovery Recommender System (2025)

Tsung-Hao Huang et al.

2.65

Feature Intensity Extractor and Predictor to Improve Recommender Systems (2026)

Bilal Hawashin et al.

1.94

Cross-domain recommender system for non-overlapping domains using clustering-based pattern transfer mechanism (2026)

A. Bhattacharya et al.

1.94

A Federated Recommendation System Framework Based on Variational Autoencoder With Mixture of Experts (2026)

Yunpeng Xiao et al.

1.94

MaRCA: Multi-Agent Reinforcement Learning for Dynamic Computation Allocation in Large-Scale Recommender Systems (2025)

Wan Jiang et al.

1.61

Theoretical Guarantees for LT-TTD: A Unified Transformer-based Architecture for Two-Level Ranking Systems (2025)

Ayoub Abraich

1.22

A Proposed Hybrid Recommender System for Tourism Industry in Iraq Using Evolutionary Apriori and K-means Algorithms (2025)

Bryar A. Hassan et al.

1.17

OpenTable data with multi-criteria ratings (2025)

Yong Zheng

1.00

A Neural Autoregressive Approach to Collaborative Filtering (2016)

Yin Zheng et al.

—

Classifier Selection with Permutation Tests (2017)

Marta Arias et al.

—

Intent-Aware Contextual Recommendation System (2017)

Biswarup Bhattacharya et al.

—

Offline A/B testing for Recommender Systems (2018)

Alexandre Gilotte et al.

—

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018)

Hongwei Wang et al.

—

Learning Contextual Bandits in a Non-stationary Environment (2018)

Qingyun Wu et al.

—

Grapevine: A Wine Prediction Algorithm Using Multi-dimensional Clustering Methods (2018)

Richard Diehl Martinez et al.

—

Context-Aware Systems for Sequential Item Recommendation (2018)

Moin Nadeem et al.

—

Inferring Complementary Products from Baskets and Browsing Sessions (2018)

Ilya Trofimov

—

Clustered Monotone Transforms for Rating Factorization (2018)

Gaurush Hiranandani et al.

—

Domain-to-Domain Translation Model for Recommender System (2018)

Linh Nguyen and Tsukasa Ishigaki

—

Deep Bayesian Multi-Target Learning for Recommender Systems (2019)

Qi Wang et al.

—

Knowledge-aware Complementary Product Representation Learning (2019)

Da Xu et al.

—

Recommending Dream Jobs in a Biased Real World (2019)

Nadia Fawaz

—

On a scalable problem transformation method for multi-label learning (2019)

Dora Jambor et al.

—

A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers (2019)

Rafael Gomes Mantovani et al.

—

The FacT: Taming Latent Factor Models for Explainability with Factorization Trees (2019)

Yiyi Tao et al.

—

Adaptive Deep Learning of Cross-Domain Loss in Collaborative Filtering (2019)

Dimitrios Rafailidis and Gerhard Weiss

—

Music Recommendations in Hyperbolic Space: An Application of Empirical Bayes and Hierarchical Poincar\'e Embeddings (2019)

Tim Schmeier et al.

—

Temporal Collaborative Ranking Via Personalized Transformer (2019)

Liwei Wu et al.

—

Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction (2019)

Hong Wen and Jing Zhang and Yuan Wang and Fuyu Lv and Wentian Bao and Quan Lin and Keping Yang

—

SWAG: Item Recommendations using Convolutions on Weighted Graphs (2019)

Amit Pande et al.

—

ragamAI: A Network Based Recommender System to Arrange a Indian Classical Music Concert (2019)

Arunkumar Bagavathi et al.

—

Balancing Accuracy and Diversity in Recommendations using Matrix Completion Framework (2020)

Anupriya Gogna and Angshul Majumdar

—

Practical Privacy Preserving POI Recommendation (2020)

Chaochao Chen et al.

—

Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect (2020)

Priyank Agrawal and Theja Tulabandhula

—

MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation (2020)

Manqing Dong and Feng Yuan and Lina Yao and Xiwei Xu and Liming Zhu

—

Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions (2020)

James McInerney et al.

—

Detecting Beneficial Feature Interactions for Recommender Systems (2020)

Yixin Su et al.

—

Shared MF: A privacy-preserving recommendation system (2020)

Senci Ying

—

Fatigue-aware Bandits for Dependent Click Models (2020)

Junyu Cao et al.

—

Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization (2020)

Rohan Anand and Joeran Beel

—

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback (2020)

Zifeng Wang and Xi Chen and Rui Wen and Shao-Lun Huang and Ercan E. Kuruoglu and Yefeng Zheng

—

Interest-Behaviour Multiplicative Network for Resource-limited Recommendation (2020)

Qianliang Wu and Tong Zhang and Zhen Cui and Jian Yang

—

Top-Rank-Focused Adaptive Vote Collection for the Evaluation of Domain-Specific Semantic Models (2020)

Pierangelo Lombardo et al.

—

Consumer Behaviour in Retail: Next Logical Purchase using Deep Neural Network (2020)

Ankur Verma

—

Ensemble Learning Based Classification Algorithm Recommendation (2021)

Guangtao Wang et al.

—

Review Polarity-wise Recommender (2021)

Han Liu et al.

—

Review of Clustering-Based Recommender Systems (2021)

Irina Beregovskaya and Mikhail Koroteev

—

DiPS: Differentiable Policy for Sketching in Recommender Systems (2021)

Aritra Ghosh et al.

—

StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random (2022)

Haoxuan Li et al.

—

$Ae^2I$: A Double Autoencoder for Imputation of Missing Values (2023)

Fuchang Gao

—

Improvements on Recommender System based on Mathematical Principles (2023)

Fu Chen et al.

—

TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback (2023)

Yuxiang Qiu et al.

—

Position Paper: Why the Shooting in the Dark Method Dominates Recommender Systems Practice; A Call to Abandon Anti-Utopian Thinking (2024)

David Rohde

—

Two-stage Risk Control with Application to Ranked Retrieval (2024)

Yunpeng Xu et al.

—

System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes (2024)

Arpit Agarwal et al.

—

CRUISE on Quantum Computing for Feature Selection in Recommender Systems (2024)

Jiayang Niu et al.

—

E-Commerce Product Recommendation System based on ML Algorithms (2024)

Md. Zahurul Haque

—

Enhancing Personalized Recipe Recommendation Through Multi-Class Classification (2024)

Harish Neelam et al.

—

Awesome Machine Learning (Statistics)

Datasets & benchmarks

Key papers

Machine Learning (Statistics)

Awesome Machine Learning (Statistics)

Datasets & benchmarks

Key papers