Embarrassingly Shallow Autoencoders for Sparse Data

8 May 2019

Papers citing "Embarrassingly Shallow Autoencoders for Sparse Data"

29 / 29 papers shown

Title
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation Chanwoo Kim Jinkyu Sung Yebonn Han Joonseok Lee GNN 47 0 0 01 May 2025
A Comparative Study of Recommender Systems under Big Data Constraints Arimondo Scrivano 30 0 0 11 Apr 2025
Why is Normalization Necessary for Linear Recommenders? Seongmin Park Mincheol Yoon Hye-young Kim Jongwuk Lee 35 0 0 08 Apr 2025
Shallow AutoEncoding Recommender with Cold Start Handling via Side Features Edward DongBo Cui Lu Zhang William Ping-hsun Lee 41 0 0 03 Apr 2025
Extending MovieLens-32M to Provide New Evaluation Objectives Mark D. Smucker Houmaan Chamani 33 0 0 02 Apr 2025
Weighted Tensor Decompositions for Context-aware Collaborative Filtering Joey De Pauw Bart Goethals 60 0 0 11 Mar 2025
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis Alireza Gharahighehi Achilleas Ghinis Michela Venturini Frederik Cornillie C. Vens 45 0 0 27 Feb 2025
Evaluating ChatGPT as a Recommender System: A Rigorous Approach Dario Di Palma Giovanni Maria Biancofiore Vito Walter Anelli Fedelucio Narducci Tommaso Di Noia E. Sciascio ALM 46 27 0 07 Sep 2023
Distributional Off-Policy Evaluation for Slate Recommendations Shreyas Chaudhari David Arbour Georgios Theocharous N. Vlassis OffRL 44 0 0 27 Aug 2023
Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders Petr Kasalický Rodrigo Alves Pavel Kordík OffRL 23 0 0 14 Aug 2023
Toward a Better Understanding of Loss Functions for Collaborative Filtering Seongmin Park Mincheol Yoon Jae-woong Lee Hogun Park Jongwuk Lee 34 14 0 11 Aug 2023
On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top- $n$ Recommendation Olivier Jeunen Ivan Potapov Aleksei Ustimenko ELM OffRL 27 11 0 27 Jul 2023
Adap- $τ$ : Adaptively Modulating Embedding Magnitude for Recommendation Jiawei Chen Junkang Wu Jiancan Wu Sheng Zhou Xuezhi Cao Xiangnan He 38 30 0 09 Feb 2023
Recommender Systems: A Primer P. Castells Dietmar Jannach OffRL 32 5 0 06 Feb 2023
Data Distillation: A Survey Noveen Sachdeva Julian McAuley DD 45 73 0 11 Jan 2023
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings Antoine Ledent Rodrigo Alves Yunwen Lei Y. Guermeur Marius Kloft 23 3 0 16 Dec 2022
Towards Reliable Item Sampling for Recommendation Evaluation Dong Li Ruoming Jin Zhenming Liu Bin Ren Jing Gao Zhi Liu 22 9 0 28 Nov 2022
Blurring-Sharpening Process Models for Collaborative Filtering Jeongwhan Choi Seoyoung Hong Noseong Park Sung-Bae Cho 17 40 0 17 Nov 2022
Multi-Objective Recommender Systems: Survey and Challenges Dietmar Jannach 24 13 0 19 Oct 2022
On the Generalizability and Predictability of Recommender Systems Duncan C. McElfresh Sujay Khandagale Jonathan Valverde John P. Dickerson Colin White 41 10 0 23 Jun 2022
Infinite Recommendation Networks: A Data-Centric Approach Noveen Sachdeva Mehak Preet Dhaliwal Carole-Jean Wu Julian McAuley DD 33 28 0 03 Jun 2022
iALS++: Speeding up Matrix Factorization with Subspace Optimization Steffen Rendle Walid Krichene Li Zhang Y. Koren 16 9 0 26 Oct 2021
On the Regularization of Autoencoders Harald Steck Dario Garcia-Garcia SSL AI4CE 30 4 0 21 Oct 2021
SimpleX: A Simple and Strong Baseline for Collaborative Filtering Kelong Mao Jieming Zhu Jinpeng Wang Quanyu Dai Zhenhua Dong Xi Xiao Xiuqiang He 18 159 0 26 Sep 2021
Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems A. Nikolakopoulos Xia Ning Christian Desrosiers George Karypis OffRL 54 29 0 09 Sep 2021
How Powerful is Graph Convolution for Recommendation? Yifei Shen Yongji Wu Yao Zhang Caihua Shan Jun Zhang Khaled B. Letaief Dongsheng Li GNN 33 100 0 17 Aug 2021
Reenvisioning Collaborative Filtering vs Matrix Factorization Vito Walter Anelli Alejandro Bellogín Tommaso Di Noia Claudio Pomo 16 26 0 28 Jul 2021
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback Ilya Shenbin Anton M. Alekseev E. Tutubalina Valentin Malykh Sergey I. Nikolenko BDL DRL 18 196 0 24 Dec 2019
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