activity
20172024
most citedCurrent Challenges and Visions in Music Recommender Systems Research

313 citations · 457 across the 6 of their papers we have counts for

collaborators

6 papers

cs.IR2024

The trade-off between data minimization and fairness in collaborative filtering

Nasim Sonboli, Sipei Li, Mehdi Elahi +1

General Data Protection Regulations (GDPR) aim to safeguard individuals' personal information from harm. While full compliance is mandatory in the European Union and the California…

cs.IR2023★ 133 cited

A Survey on Popularity Bias in Recommender Systems

Anastasiia Klimashevskaia, Dietmar Jannach, Mehdi Elahi +1

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long…

cs.SI2018

Social Network based Short-Term Stock Trading System

Paolo Cremonesi, Chiara Francalanci, Alessandro Poli +4

This paper proposes a novel adaptive algorithm for the automated short-term trading of financial instrument. The algorithm adopts a semantic sentiment analysis technique to inspect…

cs.IR2017★ 313 cited

Current Challenges and Visions in Music Recommender Systems Research

Markus Schedl, Hamed Zamani, Ching-Wei Chen +2

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all…

cs.IR2017★ 11 cited

Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation

Yashar Deldjoo, Massimo Quadrana, Mehdi Elahi +1

Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features…

cs.IR2017

Toward Active Learning in Cross-domain Recommender Systems

Roberto Pagano, Massimo Quadrana, Mehdi Elahi +1

One of the main challenges in Recommender Systems (RSs) is the New User problem which happens when the system has to generate personalised recommendations for a new user whom the s…