Recommender systems use algorithms to provide users with product or service
recommendations. Recently, these systems have been using machine learning algorithms …

A random walk is known as a random process which describes a path including a
succession of random steps in the mathematical space. It has increasingly been popular in …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

Federated learning (FL) is quickly becoming the de facto standard for the distributed training
of deep recommendation models, using on-device user data and reducing server costs. In a …

User behavior data in recommender systems are driven by the complex interactions of many
latent factors behind the users' decision making processes. The factors are highly entangled …

To learn a sequential recommender, the existing methods typically adopt the sequence-to-
item (seq2item) training strategy, which supervises a sequence model with a user's next …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building
recommender systems in industrial settings, owing to its interpretability and efficiency in real …

Social recommendation has emerged to leverage social connections among users for
predicting users' unknown preferences, which could alleviate the data sparsity issue in …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …