Recommender systems help users to find information that fits their preferences in an
overloaded search space. Collaborative filtering systems suffer from increasingly severe …

Music streaming services heavily rely on recommender systems to improve their users'
experience, by hel** them navigate through a large musical catalog and discover new …

With the convergence of the era of global news and the era of big data, the daily amount of
news sent to the world is exploding. Users also face the problem of information overloads …

Recommender systems aim to forecast users' rank, interests, and preferences in specific
products and recommend them to a user for purchase. Collaborative filtering is the most …

Because of the popularity of Internet and mobile Internet, people are facing serious
information overloading problems nowadays. Recommendation engine is very useful to help …

In collaborative filtering recommender systems, items recommended to an active user are
selected based on the interests of users similar to him/her. Collaborative filtering systems …

Identifying users with similar interest plays a vital role in building the recommendation
model. Web server log acts as a repository from which the information needed for identifying …

The purpose of recommendation systems is to help users find effective information quickly
and conveniently and also to present the items that users are interested in. While the …