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On the Difficulty of Evaluating Baselines: A Study on Recommender
  Systems

On the Difficulty of Evaluating Baselines: A Study on Recommender Systems

4 May 2019
Steffen Rendle
Li Zhang
Y. Koren
ArXivPDFHTML

Papers citing "On the Difficulty of Evaluating Baselines: A Study on Recommender Systems"

14 / 14 papers shown
Title
Algorithm Performance Spaces for Strategic Dataset Selection
Algorithm Performance Spaces for Strategic Dataset Selection
Steffen Schulz
22
0
0
29 Apr 2025
Why is Normalization Necessary for Linear Recommenders?
Why is Normalization Necessary for Linear Recommenders?
Seongmin Park
Mincheol Yoon
Hye-young Kim
Jongwuk Lee
35
0
0
08 Apr 2025
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
Hung Vinh Tran
Tong Chen
Quoc Viet Hung Nguyen
Zi-Rui Huang
Lizhen Cui
Hongzhi Yin
41
1
0
25 Jun 2024
Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models
Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models
Jorge Paz-Ruza
Amparo Alonso-Betanzos
B. Guijarro-Berdiñas
Brais Cancela
Carlos Eiras-Franco
56
2
0
19 Jan 2024
On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation
  Metric for Top-$n$ Recommendation
On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-nnn Recommendation
Olivier Jeunen
Ivan Potapov
Aleksei Ustimenko
ELM
OffRL
27
11
0
27 Jul 2023
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood
  Estimation for Latent Gaussian Models
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
Alexander Lin
Bahareh Tolooshams
Yves Atchadé
Demba E. Ba
31
1
0
05 Jun 2023
Symphony in the Latent Space: Provably Integrating High-dimensional
  Techniques with Non-linear Machine Learning Models
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models
Qiong Wu
Jian Li
Zhenming Liu
Yanhua Li
Mihai Cucuringu
28
4
0
01 Dec 2022
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
Josh Gardner
Zoran Popovic
Ludwig Schmidt
OOD
24
22
0
23 Nov 2022
The effectiveness of factorization and similarity blending
The effectiveness of factorization and similarity blending
Andrea Pinto
Giacomo Camposampiero
Loic Houmard
Marc Lundwall
18
0
0
16 Sep 2022
Preference Dynamics Under Personalized Recommendations
Preference Dynamics Under Personalized Recommendations
Sarah Dean
Jamie Morgenstern
72
34
0
25 May 2022
Private Alternating Least Squares: Practical Private Matrix Completion
  with Tighter Rates
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Steve Chien
Prateek Jain
Walid Krichene
Steffen Rendle
Shuang Song
Abhradeep Thakurta
Li Zhang
22
19
0
20 Jul 2021
On component interactions in two-stage recommender systems
On component interactions in two-stage recommender systems
Jiri Hron
K. Krauth
Michael I. Jordan
Niki Kilbertus
CML
LRM
32
31
0
28 Jun 2021
Modurec: Recommender Systems with Feature and Time Modulation
Modurec: Recommender Systems with Feature and Time Modulation
Javier Maroto
Clément Vignac
P. Frossard
10
1
0
13 Oct 2020
A Troubling Analysis of Reproducibility and Progress in Recommender
  Systems Research
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research
Maurizio Ferrari Dacrema
Simone Boglio
Paolo Cremonesi
Dietmar Jannach
15
196
0
18 Nov 2019
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