ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2008.13526
  4. Cited By
Theoretical Modeling of the Iterative Properties of User Discovery in a
  Collaborative Filtering Recommender System

Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System

21 August 2020
Sami Khenissi
M. Boujelbene
O. Nasraoui
ArXivPDFHTML

Papers citing "Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System"

5 / 5 papers shown
Title
Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization
Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization
Hongxu Wang
Zhu Sun
Yingpeng Du
Lu Zhang
Tiantian He
Y. Ong
56
0
0
18 Feb 2025
A Simple Yet Effective Approach for Diversified Session-Based
  Recommendation
A Simple Yet Effective Approach for Diversified Session-Based Recommendation
Qing Yin
Hui Fang
Zhu Sun
Yew-Soon Ong
19
1
0
30 Mar 2024
Metrics for popularity bias in dynamic recommender systems
Metrics for popularity bias in dynamic recommender systems
Valentijn Braun
D. Bhaumik
Diptish Dey
18
0
0
12 Oct 2023
Debiased Explainable Pairwise Ranking from Implicit Feedback
Debiased Explainable Pairwise Ranking from Implicit Feedback
Khalil Damak
Sami Khenissi
O. Nasraoui
13
16
0
30 Jul 2021
How Algorithmic Confounding in Recommendation Systems Increases
  Homogeneity and Decreases Utility
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
A. Chaney
Brandon M Stewart
Barbara E. Engelhardt
CML
169
312
0
30 Oct 2017
1