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Critically Examining the Claimed Value of Convolutions over User-Item
  Embedding Maps for Recommender Systems

Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems

23 July 2020
Maurizio Ferrari Dacrema
Federico Parroni
Paolo Cremonesi
Dietmar Jannach
ArXivPDFHTML

Papers citing "Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems"

4 / 4 papers shown
Title
How Expressive are Graph Neural Networks in Recommendation?
How Expressive are Graph Neural Networks in Recommendation?
Xuheng Cai
Lianghao Xia
Xubin Ren
Chao Huang
38
6
0
22 Aug 2023
A Review on Pushing the Limits of Baseline Recommendation Systems with
  the integration of Opinion Mining & Information Retrieval Techniques
A Review on Pushing the Limits of Baseline Recommendation Systems with the integration of Opinion Mining & Information Retrieval Techniques
D. Piyadigama
Guhanathan Poravi
VLM
14
0
0
03 May 2022
Reenvisioning Collaborative Filtering vs Matrix Factorization
Reenvisioning Collaborative Filtering vs Matrix Factorization
Vito Walter Anelli
Alejandro Bellogín
Tommaso Di Noia
Claudio Pomo
16
26
0
28 Jul 2021
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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