Collaborative Filtering under Model Uncertainty

2020-08-23 · Robin M. Schmidt, Moritz Hahn

In their work, Dean, Rich, and Recht create a model to research recourse and availability of items in a recommender system. We used the definition of predictive multiplicity by Marx, Pin Calmon, and Ustun to examine different variations of this model, using different values for two model parameters. Pairwise comparison of their models show, that most of these models produce very similar results in terms of discrepancy and ambiguity for the availability and only in some cases the availability sets differ significantly.

📄 PDF Abstract BibTeX arXiv:2008.10117

Code (2)

SirRob1997/collaborative_filtering 공식 구현

SirRob1997/Collaborative-Filtering-under-Model-Uncertainty

Tasks

Collaborative FilteringmodelRecommendation Systems

Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty

2024-04-09 · Haoxuan Li, Yuanxin Ouyang, Zhuang Liu, Wenge Rong 외

Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fi…

Collaborative Filtering Graph Attention Recommendation Systems

Federated Data-Driven Kalman Filtering for State Estimation

2024-11-06 · Nikos Piperigkos, Alexandros Gkillas, Christos Anagnostopoulos, Aris S. Lalos

This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More specifically, we build on the standard appr…

Autonomous Driving Autonomous Vehicles Decision Making Federated Learning+2

Matrix Completion under Interval Uncertainty

2014-08-11 · Jakub Marecek, Peter Richtarik, Martin Takac

Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show t…

Collaborative Filtering Matrix Completion

Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems

2021-06-08 · Yinchong Yang, Florian Buettner

Recommender systems are often designed based on a collaborative filtering approach, where user preferences are predicted by modelling interactions between users and items. Many common approaches to solve the collaborativ…

Collaborative Filtering Gaussian Processes Recommendation Systems Representation Learning

Enhanced Recommendation Combining Collaborative Filtering and Large Language Models

2024-12-25 · Xueting Lin, Zhan Cheng, Longfei Yun, Qingyi Lu 외

With the advent of the information explosion era, the importance of recommendation systems in various applications is increasingly significant. Traditional collaborative filtering algorithms are widely used due to their …

Collaborative Filtering Diversity Natural Language Understanding Recommendation Systems