With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

Recommender systems are effective tools of information filtering that are prevalent due to
increasing access to the Internet, personalization trends, and changing habits of computer …

These days, many recommender systems (RS) are utilized for solving information overload
problem in areas such as e-commerce, entertainment, and social media. Although classical …

Automatic recommendation has become an increasingly relevant problem to industries,
which allows users to discover new items that match their tastes and enables the system to …

In the past decade, recommender systems have been widely used to provide users with
personalized products and services. However, most traditional recommender systems are …

With the widespread application of service-oriented architecture (SOA), a flood of similarly
functioning services have been deployed online. How to recommend services to users to …

A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative
and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful …

Neighborhood regularization is highly important for a latent factor (LF)-based Quality-of-
Service (QoS)-predictor since similar users usually experience similar QoS when invoking …

With the rapid development of service-oriented computing and cloud computing, an
increasing number of Web services have been published on the Internet, which makes it …

Session-based recommendation focuses on the prediction of user actions based on
anonymous sessions and is a necessary method in the lack of user historical data. However …