Collaborative filtering is the most popular approach for recommender systems. One way to
perform collaborative filtering is matrix factorization, which characterizes user preferences …

Collaborative filtering inherently suffers from the data sparsity and cold start problems.
Social networks have been shown useful to help alleviate these issues. However, social …

Collaborative filtering is one of the most common scenarios and popular research topics in
recommender systems. Among existing methods, latent factor models, ie, learning a specific …

Matrix factorization is one of the most efficient approaches in recommender systems.
However, such algorithms, which rely on the interactions between users and items, perform …

Recommender systems are considered as a promising approach to solve the problem of
information overload. In collaborative filtering recommender systems, one of the most …

Classical matrix factorization approaches to collaborative filtering learn a latent vector for
each user and each item, and recommendations are scored via the similarity between two …

Recommendation systems suggest items based on user preferences. Collaborative filtering
is a popular approach in which recommending is based on the rating history of the system …

A widely adopted paradigm in the design of recommender systems is to represent users and
items as vectors, often referred to as latent factors or embeddings. Embeddings can be …

Collaborative filtering (CF) aims to build a model from users' past behaviors and/or similar
decisions made by other users, and use the model to recommend items for users. Despite of …

Matrix factorization-based methods are among the most popular methods for collaborative
filtering tasks with implicit feedback. The most effective of these methods do not apply sign …