SimpleX: A simple and strong baseline for collaborative filtering
Collaborative filtering (CF) is a widely studied research topic in recommender systems. The
learning of a CF model generally depends on three major components, namely interaction …
learning of a CF model generally depends on three major components, namely interaction …
Incorporating bias-aware margins into contrastive loss for collaborative filtering
Collaborative filtering (CF) models easily suffer from popularity bias, which makes
recommendation deviate from users' actual preferences. However, most current debiasing …
recommendation deviate from users' actual preferences. However, most current debiasing …
Towards representation alignment and uniformity in collaborative filtering
Collaborative filtering (CF) plays a critical role in the development of recommender systems.
Most CF methods utilize an encoder to embed users and items into the same representation …
Most CF methods utilize an encoder to embed users and items into the same representation …
Incremental collaborative filtering recommender based on regularized matrix factorization
X Luo, Y Xia, Q Zhu - Knowledge-Based Systems, 2012 - Elsevier
The Matrix-Factorization (MF) based models have become popular when building
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …
Deep collaborative filtering via marginalized denoising auto-encoder
Collaborative filtering (CF) has been widely employed within recommender systems to solve
many real-world problems. Learning effective latent factors plays the most important role in …
many real-world problems. Learning effective latent factors plays the most important role in …
A hybrid collaborative filtering model with deep structure for recommender systems
Collaborative filtering (CF) is a widely used approach in recommender systems to solve
many real-world problems. Traditional CF-based methods employ the user-item matrix …
many real-world problems. Traditional CF-based methods employ the user-item matrix …
Enhancing collaborative filtering with generative augmentation
Collaborative filtering (CF) has become one of the most popular and widely used methods in
recommender systems, but its performance degrades sharply for users with rare interaction …
recommender systems, but its performance degrades sharply for users with rare interaction …
Coupledcf: Learning explicit and implicit user-item couplings in recommendation for deep collaborative filtering
© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Non-IID
recommender system discloses the nature of recommendation and has shown its potential …
recommender system discloses the nature of recommendation and has shown its potential …
Collaborative deep ranking: A hybrid pair-wise recommendation algorithm with implicit feedback
Abstract Collaborative Filtering with Implicit Feedbacks (eg, browsing or clicking records),
named as CF-IF, is demonstrated to be an effective way in recommender systems. Existing …
named as CF-IF, is demonstrated to be an effective way in recommender systems. Existing …
Deep latent factor model for collaborative filtering
A Mongia, N Jhamb, E Chouzenoux, A Majumdar - Signal Processing, 2020 - Elsevier
Latent factor models have been used widely in collaborative filtering based recommender
systems. In recent years, deep learning has been successful in solving a wide variety of …
systems. In recent years, deep learning has been successful in solving a wide variety of …