Recommender systems have been widely applied to several domains and applications to
assist decision making by recommending items tailored to user preferences. One of the …

Recommender systems have become essential in modern information systems and Internet
applications by delivering personalized and pertinent content to users. While conventional …

This paper demonstrates Fair-SRS, a Fair Session-based Recommendation System that
predicts user's next click based on their historical and current sessions. Fair-SRS provides …

Traditional recommender systems mainly focus on the accuracy of recommendation, which
lead to recommender systems reinforcing popular items and ignoring lesser-known items …

There have been many research efforts aimed at improving recommendation accuracy with
Collaborative Filtering (CF). Yet there is still a lack of investigation into the integration of CF …

The main focus of our work is the problem of multiple objectives optimization (MOO) while
providing a final list of recommendations to the user. Currently, system designers can tune …

Ranking is a prerequisite for making decisions, and therefore it is a very responsible and
frequently applied activity. This study considers fairness issues in a multi-criteria decision …

Australia's largest bank, Commonwealth Bank (CBA) has a large data and analytics function
that focuses on building a brighter future for all using data and decision science. In this work …

In the digital era, recommender systems (RS) have become an integral part of our daily
interactions, exerting a significant impact on users and society. However, this also raises …

Abstract Session-based Recommender Systems (SRSs), which aim to recommend users'
next action based on their current and historical sessions, play a significant role in many real …