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Found 8 papers, 2 papers with code
Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems
Graph-based collaborative filtering methods have prevailing performance for recommender systems since they can capture high-order information between users and items, in which the graphs are constructed from the observed user-item interactions that might miss links or contain spurious positive interactions in industrial scenarios.
Towards Automated Negative Sampling in Implicit Recommendation
Hence, we propose a hypothesis that the negative sampler should align with the capacity of the recommendation models as well as the statistics of the datasets to achieve optimal performance.
Compressed Interaction Graph based Framework for Multi-behavior Recommendation
However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''.
A Survey on User Behavior Modeling in Recommender Systems
Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions.
On Batch-size Selection for Stochastic Training for Graph Neural Networks
In recent years deep learning has become an important framework for supervised learning.
A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks
Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.
Learning Privately over Distributed Features: An ADMM Sharing Approach
Distributed machine learning has been widely studied in order to handle exploding amount of data.
Stochastic Distributed Optimization for Machine Learning from Decentralized Features
Distributed machine learning has been widely studied in the literature to scale up machine learning model training in the presence of an ever-increasing amount of data.
