Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …

Due to underlying privacy-sensitive information in user-item interaction data, the risk of
privacy leakage exists in the centralized-training recommender system (RecSys). To this …

Sequential recommender systems often struggle with accurate personalized
recommendations due to data sparsity issues. Existing works use variational autoencoders …

Previous federated recommender systems are based on traditional matrix factorization,
which can improve personalized service but are vulnerable to gradient inference attacks …

Gradient boosting decision tree (GBDT) is a widely used ensemble algorithm in the industry.
Its vertical federated learning version, SecureBoost, is one of the most popular algorithms …

Recommender systems have gained immense popularity in recent years for predicting
users' interests by learning embeddings. The majority of existing recommendation …

Federated recommendation (FR) is a decentralised approach to training personalised
recommender systems, protecting users' privacy by avoiding data collection. Despite its …

Resource recommendation in edge computing relies on distributed resource alignment
across multiple servers and interconnected networks. Consequently, addressing issues …

Cross-platform recommendation aims to improve recommendation accuracy by gathering
heterogeneous features from different platforms. However, such cross-silo collaborations …

Non-negative matrix factorization (NMF) with missing-value completion is a well-known
effective Collaborative Filtering (CF) method used to provide personalized user …