Collaborative Filtering
381 papers with code • 2 benchmarks • 4 datasets
Libraries
Use these libraries to find Collaborative Filtering models and implementations
Gowalla
Yelp2018
Most implemented papers
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
NeverInAsh/fit-recommendation
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To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation.
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering.
Neural Collaborative Reasoning
Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i. e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions.
Learning User Representations with Hypercuboids for Recommender Systems
d2l-ai/d2l-tr
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Furthermore, we present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests.
Federated Reconstruction: Partially Local Federated Learning
google-research/federated
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NeurIPS 2021
We also describe the successful deployment of this approach at scale for federated collaborative filtering in a mobile keyboard application.
GLocal-K: Global and Local Kernels for Recommender Systems
usydnlp/Glocal_K
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Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item.
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering
This work explores the reproducibility of CFGAN.
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists.
Latent Dirichlet Allocation
Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.
Matrix Completion on Graphs
Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs.
