- Item-Based Top-N Recommendation Algorithms
- A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
- Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions
- Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
- Item-Based Collaborative Filtering Recommendation Algorithms
- Restricted Boltzmann Machines for Collaborative Filtering
- A Survey of Collaborative Filtering Techniques
- Implicit Feedback for Recommender Systems
- Building Industrial-scale Real-world Recommender Systems
- A Generic Coordinate Descent Framework for Learning from Implicit Feedback
- A Contextual-Bandit Approach to Personalized News Article Recommendation
- One-Class Collaborative Filtering
- Online-Updating Regularized Kernel Matrix Factorization Models for Large-Scale Recommender Systems {PPT}
- Context-Aware Recommender Systems[1]
- Context-Aware Recommender Systems[2]
- Collaborative Filtering with Temporal Dynamics
- An MDP-Based Recommender System[1]
- An MDP-based Recommender System[2]
- DRN: A Deep Reinforcement Learning Framework for News Recommendation
- Collaborative Filtering with Recurrent Neural Networks
- Training Deep AutoEncoders for Collaborative Filtering
- StarSpace: Embed All The Things!
- Deep & Cross Network for Ad Click Predictions
- Attentive collaborative filtering: Multimedia recommendation with feature- and item-level attention
- Context-aware open relation extraction with factorization machines
- Fast context-aware recommendations with factorization machines
- Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach
- Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering
- Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
- AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders
- Local Implicit Feedback Mining for Music Recommendation
- Deep Session Interest Network for Click-Through Rate Prediction
- AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
- FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
- NFFM: Operation-aware Neural Networks for User Response Prediction
- Convolutional Sequence to Sequence Learning
- Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
- Feature Hashing for Large Scale Multitask Learning
- Optimization Methods for Large-Scale Machine Learning
- Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors’ Demonstration
- class imbalance and cost sensitivity why under-sampling beats over-sampling
- Next Item Recommendation with Self-Attention
- Deep Reinforcement Learning for List-wise Recommendations
- Deep Reinforcement Learning for Page-wise Recommendations
- Top-K Off-Policy Correction for a REINFORCE Recommender System
- Deep Learning Based Text Classification: A Comprehensive Review
- A Survey on Knowledge Graph-Based Recommender Systems
- A Survey on Multi-Task Learning
- Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
- High-Dimensional Continuous Control Using Generalized Advantage Estimation
- Deep Reinforcement Learning for Online Advertising in Recommender Systems
- Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
- Factorizing Personalized Markov Chains for Next-Basket Recommendation
- Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
- Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations