Recommender systems employ recommendation algorithms to predict users' preferences to
items. These preferences are often represented as numerical ratings. However, existing …

Recommender systems attempt to guide users in decisions related to choosing items based
on inferences about their personal opinions. Most existing systems implicitly assume the …

Due to the exponential growth of information on the Web, Recommender Systems have
been developed to generate suggestions to help users overcome information overload and …

Recommender systems aim to identify items that a user may like. In this paper, we discuss a
three-way decision approach which provides a more meaningful way to recommend items to …

In recent years, there has been a growing demand to develop efficient recommender
systems which track users' preferences and recommend potential items of interest to users …

Nowadays, the increasing demand for group recommendations can be observed. In this
paper we address the problem of recommendation performance for groups of users (group …

Much early evaluation work focused specifically on the" accuracy" of recommendation
algorithms. Good recommendation (in terms of accuracy) has, however, to be coupled with …

Traditionally, recommender systems for the web deal with applications that have two
dimensions, users and items. Based on access data that relate these dimensions, a …

The collaborative filtering approach to recommender systems predicts user preferences for
products or services by learning past user-item relationships. In this work, we propose novel …

Recently, linear regression models have shown to often produce rather competitive results
against more sophisticated deep learning models. Meanwhile, the (weighted) matrix …