Simplify and Robustify Negative Sampling for Implicit Collaborative
Filtering

Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering

7 September 2020

Jingtao Ding

Yong Li

Depeng Jin

Papers citing "Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering"

8 / 8 papers shown

Title
MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation Xiangjin Xie Y. Chen Ruipeng Wang Kai Ouyang Zihan Zhang ... Buyue Qian Hansen Zheng Bo Hu Chengxiang Zhuo Zang Li 68 3 0 12 Feb 2025
AutoSAM: Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems H. Zhang Mingyue Cheng Qi Liu Z. Liu Junzhe Jiang Enhong Chen AI4TS 44 3 0 03 Jan 2025
End-to-end Training for Recommendation with Language-based User Profiles Zhaolin Gao Joyce Zhou Yijia Dai Thorsten Joachims AI4Ed 59 3 0 24 Oct 2024
Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity H. Chung Jungtaek Kim Hyungeun Jo Hyungwon Choi 37 0 0 01 Aug 2024
Toward a Better Understanding of Loss Functions for Collaborative Filtering Seongmin Park Mincheol Yoon Jae-woong Lee Hogun Park Jongwuk Lee 24 13 0 11 Aug 2023
Distillation from Heterogeneous Models for Top-K Recommendation SeongKu Kang Wonbin Kweon Dongha Lee Jianxun Lian Xing Xie Hwanjo Yu VLM 27 21 0 02 Mar 2023
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering SeongKu Kang Dongha Lee Wonbin Kweon Junyoung Hwang Hwanjo Yu 11 12 0 26 Feb 2022
Scalable Personalised Item Ranking through Parametric Density Estimation Riku Togashi Masahiro Kato Mayu Otani T. Sakai Shiníchi Satoh 33 0 0 11 May 2021