Most existing collaborative filtering techniques have focused on modeling the binary relation
of users to items by extracting from user ratings. Aside from users' ratings, their affiliated …

Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …

The bipartite graph is a ubiquitous data structure that can model the relationship between
two entity types: for instance, users and items, queries and webpages. In this paper, we …

Collaborative filtering (CF) is an important and popular technology for recommender
systems. However, current CF methods suffer from such problems as data sparsity …

Event-based social networks (EBSNs), such as Meetup and Plancast, which offer platforms
for users to plan, arrange, and publish events, have gained increasing popularity and rapid …

User-based and item-based collaborative filtering (CF) methods are two of the most widely
used techniques in recommender systems. While these algorithms are widely used in both …

With the incessant growth of web services on the Internet, how to design effective web
service recommendation technologies based on Quality of Service (QoS) is becoming more …

Recommender systems have recently been singled out as a fascinating area of research,
owing to the technological progress in mobile devices, such as smartphones and tablets, as …

A recommender system uses information about known associations between users and
items to compute for a given user an ordered recommendation list of items which this user …

In this article we present a context-aware recommendation method that exploits graph-
based data models and Personalized PageRank to provide users with recommendations. In …