Collaborative Filtering
369 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Collaborative Filtering models and implementations
Gowalla
Yelp2018
Latest papers
Cluster-based Graph Collaborative Filtering
This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them.
Countering Mainstream Bias via End-to-End Adaptive Local Learning
jp-25/end-to-end-adaptive-local-leanring-tall-
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In this paper, we identify two root causes of this mainstream bias: (i) discrepancy modeling, whereby CF algorithms focus on modeling mainstream users while neglecting niche users with unique preferences; and (ii) unsynchronized learning, where niche users require more training epochs than mainstream users to reach peak performance.
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Traditional recommender systems (RS) have used user-item rating histories as their primary data source, with collaborative filtering being one of the principal methods.
KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering
sisinflab/kguf
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The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filtering (GCF).
Sequential Recommendation with Latent Relations based on Large Language Model
ysh-1998/lrd
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Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items.
Lightweight Embeddings for Graph Collaborative Filtering
Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods.
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
u-rara/afdgcf
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Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms.
Knowledge-aware Dual-side Attribute-enhanced Recommendation
tjtp/kdar
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Specifically, we build \textit{user preference representations} and \textit{attribute fusion representations} upon the attribute information in knowledge graphs, which are utilized to enhance \textit{collaborative filtering} (CF) based user and item representations, respectively.
Knowledge-Enhanced Recommendation with User-Centric Subgraph Network
Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences.
Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation
The fine-grained structured sparsity causes a large amount of unnecessary operations during both matrix multiplication and latent factor update, increasing the computational time of the MF training process.
