The task of point of interest (POI) recommendation aims to recommend unvisited places to
users based on their check-in history. A major challenge in POI recommendation is data …

Traditional collaborative filtering techniques suffer from the data sparsity problem in practice.
That is, only a small proportion of all items in the recommender system occur in a user's …

Traditional collaborative ltering assumes the availability of explicit ratings of users for items.
However, in many cases these ratings are not available and only binary, positive-only data …

In collaborative filtering, matrix factorization and collaborative metric learning are challenged
by situations where non-preferred items may appear so close to a user in the feature …

This paper presents a novel application of sentiment analysis to recommender systems
relying on explicit one-class user feedback (favorites or likes), namely joint models of unary …

Top-N recommendation systems are useful in many real world applications such as E-
commerce platforms. Most previous methods produce top-N recommendations based on the …

Modeling contextual information is a vital part of develo** effective recommender systems.
Still, existing work on recommendation algorithms has generally put limited focus on the …

The increasing availability of implicit feedback datasets has raised the interest in develo**
effective collaborative filtering techniques able to deal asymmetrically with unambiguous …

The collaborative filtering technology used in traditional recommendation systems has a
problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes …

In many personalized recommendation problems available data consists only of positive
interactions (implicit feedback) between users and items. This problem is also known as One …