Collaborative Filtering
372 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Collaborative Filtering models and implementations
Gowalla
Yelp2018
Latest papers
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.
Deep Rating Elicitation for New Users in Collaborative Filtering
Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations.
Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation with Interpretability
enoche/dgvae
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While the incorporation of multimodal information could enhance the interpretability of these systems, current multimodal models represent users and items utilizing entangled numerical vectors, rendering them arduous to interpret.
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e. g., products, jobs, news, video) and their providers.
General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout
However, we have discovered that this aggregation mechanism comes with a drawback, which amplifies biases present in the interaction graph.
Prototypical Contrastive Learning through Alignment and Uniformity for Recommendation
oceanlvr/protoau
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Specifically, we first propose prototypes (cluster centroids) as a latent space to ensure consistency across different augmentations from the origin graph, aiming to eliminate the need for random sampling of contrastive pairs.
CF4J: Collaborative Filtering for Java
Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem.
RecDCL: Dual Contrastive Learning for Recommendation
thudm/recdcl
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In this work, we investigate how to employ both batch-wise CL (BCL) and feature-wise CL (FCL) for recommendation.
