Collaborative Filtering
370 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Collaborative Filtering models and implementations
Gowalla
Yelp2018
Most implemented papers
Item2Vec: Neural Item Embedding for Collaborative Filtering
massquantity/LibRecommender
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Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities.
Embarrassingly Shallow Autoencoders for Sparse Data
Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems.
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive -- potentially occupying hundreds of gigabytes in large-scale settings.
Collaborative Memory Network for Recommendation Systems
tebesu/CollaborativeMemoryNetwork
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We propose Collaborative Memory Networks (CMN), a deep architecture to unify the two classes of CF models capitalizing on the strengths of the global structure of latent factor model and local neighborhood-based structure in a nonlinear fashion.
Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
xiangwang1223/knowledge_graph_attention_network
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Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items.
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
sb-ai-lab/RePlay
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The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards.
Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing
To the best of our knowledge, this is the first work to suggest an encoder-decoder model for knowledge tracing that applies deep self-attentive layers to exercises and responses separately.
Hybrid Recommender System based on Autoencoders
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings.
Neural Collaborative Filtering vs. Matrix Factorization Revisited
google-research/google-research
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This approach is often referred to as neural collaborative filtering (NCF).
Feature-Weighted Linear Stacking
Ensemble methods, such as stacking, are designed to boost predictive accuracy by blending the predictions of multiple machine learning models.
