Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks

ACM Conference on Recommender Systems (RecSys), 2016

14 July 2016

Evgeny Frolov

Ivan Oseledets

ArXiv (abs)PDF HTML

Papers citing "Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks"

7 / 7 papers shown

Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential PatternsACM Conference on Recommender Systems (RecSys), 2025

Veronika Ivanova

Evgeny Frolov

Alexey Vasilev

20 Aug 2025

Learning from Negative User Feedback and Measuring Responsiveness for Sequential RecommendersACM Conference on Recommender Systems (RecSys), 2023

...

123

23 Aug 2023

Tensor-based Collaborative Filtering With Smooth Ratings Scale

10 May 2022

SiReN: Sign-Aware Recommendation Using Graph Neural Networks

186

19 Aug 2021

New approach to MPI program execution time prediction

30 Jul 2020

Operationalizing the Legal Principle of Data Minimization for PersonalizationAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020

165

28 May 2020

Revealing the Unobserved by Linking Collaborative Behavior and Side Knowledge

Evgeny Frolov

Ivan Oseledets

FedML

27 Jul 2018