Recommender Systems deal with the issue of overloading information by retrieving the most
relevant sources in the wide range of web services. They help users by predicting their …

In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …

Providing recommendations to users in every facet of technology is of prior importance for all
online and offline web platforms. There are several types of recommender systems, like …

It is the era of information explosion and overload. The recommender systems can help
people quickly get the expected information when facing the enormous data flood …

Nowadays e-commerce has spread all over the world. The e-shops are not similar to the
physical shops. The e-shops can have hundreds or thousands of items independent of …

Item-to-item collaborative filtering (short for ICF) has been widely used in ecommerce
websites due to his interpretability and simplicity in real-time personalized recommendation …

Recommender systems, which provide users with recommendations of content suited to
their needs, have received great attention in today's online business world. However, most …

With the rapid development and application of the mobile Internet, huge amounts of user
data are generated and collected every day. How to take full advantages of these ubiquitous …

Recommender systems, providing users with personalized recommendations from a
plethora of choices, have been an important component for e-commerce applications to …

Collaborative Filtering (CF) has intrigued several researchers whose goal is to enhance
Recommender System's performance by mitigating their drawbacks. CF's common idea is to …