Complex networks have emerged as a simple yet powerful framework to represent and
analyze a wide range of complex systems. The problem of ranking the nodes and the edges …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

As big-data-driven complex systems, commercial recommendation systems (RSs) have
been widely used in such companies as Amazon and Ebay. Their core aim is to maximize …

Recommendation based on heterogeneous information network (HIN) is attracting more and
more attention due to its ability to emulate collaborative filtering, content-based filtering …

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

Recommendation systems are widely used to promote product visibility and sales. However,
past research suggests that they primarily benefit market superstars and therefore, can be …

Link prediction provides methods for estimating potential connections in complex networks
that have theoretical and practical relevance for personalized recommendations and various …

A network is a typical expressive form of representing complex systems in terms of vertices
and links, in which the pattern of interactions amongst components of the network is intricate …

Recommender Systems (RS) currently represent a fundamental tool in online services,
especially with the advent of Online Social Networks (OSN). In this case, users generate …

Pharmaceutical co-crystals represent a growing class of crystal forms in the context of
pharmaceutical science. They are attractive to pharmaceutical scientists because they …