Abstract In the Big Data Era, recommender systems perform a fundamental role in data
management and information filtering. In this context, Collaborative Filtering (CF) persists as …

In an era dominated by information overload, effective recommender systems are essential
for managing the deluge of data across digital platforms. Multi-stage cascade ranking …

As living data growing and evolving rapidly, traditional machine learning algorithms are hard
to update models when dealing with new training data. When new data arrives, traditional …

The primary goal of recommender systems is to identify and propose items that users might
find appealing. A large number of these systems are heavily dependent on explicit …

Aiming at the problem that the traditional collaborative filtering algorithm using shallow
models cannot learn the deep features of users and items, and the recommendation model …

Recommender systems are becoming more essential than ever as the data available online
is increasing manifold. The increasing data presents us with an opportunity to build complex …

E-commerce is the most essential application for conducting business transactions.
Delivering product information to customers require an essential machine called …

Learning by integrating multiple heterogeneous data sources is a common requirement in
many tasks. Collective Matrix Factorization (CMF) is a technique to learn shared latent …

An improved recommendation algorithm based on Conditional Variational Autoencoder
(CVAE) and Constrained Probabilistic Matrix Factorization (CPMF) is proposed to address …

Recommender systems help individuals in a community to find information or items that are
most likely to meet their needs. In this paper, we propose a new recommendation model …