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Toward a Better Understanding of Loss Functions for Collaborative
  Filtering

Toward a Better Understanding of Loss Functions for Collaborative Filtering

11 August 2023
Seongmin Park
Mincheol Yoon
Jae-woong Lee
Hogun Park
Jongwuk Lee
ArXivPDFHTML

Papers citing "Toward a Better Understanding of Loss Functions for Collaborative Filtering"

6 / 6 papers shown
Title
Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias
Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias
Miaomiao Cai
Lei Chen
Yifan Wang
Zhiyong Cheng
M. Zhang
Meng Wang
41
0
0
30 Mar 2025
RAU: Towards Regularized Alignment and Uniformity for Representation Learning in Recommendation
RAU: Towards Regularized Alignment and Uniformity for Representation Learning in Recommendation
Xi Wu
Dan Zhang
Chao Zhou
Liangwei Yang
Tianyu Lin
Jibing Gong
44
0
0
24 Mar 2025
Understanding and Scaling Collaborative Filtering Optimization from the
  Perspective of Matrix Rank
Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
Donald Loveland
Xinyi Wu
Tong Zhao
Danai Koutra
Neil Shah
Mingxuan Ju
34
1
0
15 Oct 2024
SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering
SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering
Xiaodong Yang
Huiyuan Chen
Yuchen Yan
Yuxin Tang
Yuying Zhao
Eric Xu
Yiwei Cai
Hanghang Tong
18
3
0
23 Jun 2024
Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias
Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias
Miaomiao Cai
Lei Chen
Yifan Wang
Haoyue Bai
Peijie Sun
Le Wu
Min Zhang
Meng Wang
21
10
0
31 May 2024
When Newer is Not Better: Does Deep Learning Really Benefit
  Recommendation From Implicit Feedback?
When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?
Yushun Dong
Jundong Li
Tobias Schnabel
38
9
0
02 May 2023
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