activity
20192022
most citedTop-N Recommendation Algorithms: A Quest for the State-of-the-Art

51 citations · 90 across the 3 of their papers we have counts for

collaborators

5 papers

cs.IR202251 cited

Top-N Recommendation Algorithms: A Quest for the State-of-the-Art

Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia +2

Research on recommender systems algorithms, like other areas of applied machine learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of acc…

cs.IR20212 cited

Adherence and Constancy in LIME-RS Explanations for Recommendation

Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia +3

Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence. In particular, post-hoc approaches have proved to be t…

cs.IR202137 cited

Reenvisioning Collaborative Filtering vs Matrix Factorization

Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia +1

Collaborative filtering models based on matrix factorization and learned similarities using Artificial Neural Networks (ANNs) have gained significant attention in recent years. Thi…

cs.IR2021

Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara +5

Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of prop…

cs.IR2019

On the discriminative power of Hyper-parameters in Cross-Validation and how to choose them

Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio +2

Hyper-parameters tuning is a crucial task to make a model perform at its best. However, despite the well-established methodologies, some aspects of the tuning remain unexplored. As…