Many problem settings in machine learning are concerned with the simultaneous prediction
of multiple target variables of diverse type. Amongst others, such problem settings arise in …

In most real-world scenarios, the ultimate goal of recommender system applications is to
suggest a short ranked list of items, namely top-N recommendations, that will appeal to the …

The goal of video understanding is to develop algorithms that enable machines understand
videos at the level of human experts. Researchers have tackled various domains including …

We present a simple and scalable algorithm for top-N recommendation able to deal with
very large datasets and (binary rated) implicit feedback. We focus on memory-based …

Personalized recommendation systems are used in a wide variety of applications such as
electronic commerce, social networks, web search, and more. Collaborative filtering …

In this paper we consider the collaborative ranking setting: a pool of users each provides a
set of pairwise preferences over a small subset of the set of d possible items; from these we …

Data sparsity, that is a common problem in neighbor-based collaborative filtering domain,
usually complicates the process of item recommendation. This problem is more serious in …

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare,
Brightkite, and so on, have attracted millions of users to share their social friendship and …

Recommender systems usually need to compare a large number of items before users' most
preferred ones can be found This process can be very costly if recommendations are …

The goal of collaborative filtering is to get accurate recommendations at the top of the list for
a set of users. From such a perspective, collaborative ranking based formulations with …