Federated masked matrix factorization could protect the data privacy in federated recommender systems without sacrificing efficiency or efficacy.
Though many algorithms, such as AMSGRAD and ADAMNC, have been proposed to fix the non-convergence issues, achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these methods.
Matrix Factorization has been very successful in practical recommendation applications and e-commerce.
When making cloth decisions, people usually show preferences for different semantic attributes (e. g., the clothes with v-neck collar).
Ranked #2 on Recommendation Systems on Amazon Fashion (using extra training data)
An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content.
In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR).
To solve the cold-start problem, we propose CityTransfer, which transfers chain store knowledge from semantically-relevant domains (e. g., other cities with rich knowledge, similar chain enterprises in the target city) for chain store placement recommendation in a new city.
As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure.
Graph is an important data representation which appears in a wide diversity of real-world scenarios.
Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information.
Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links.
Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space.