Neural Collaborative Filtering

Neural Collaborative Filtering (NCF) is a machine learning technique used to predict user preferences by leveraging the collaborative nature of user-item interactions. Current research focuses on enhancing NCF's capabilities through various model architectures, including those incorporating graph neural networks, multimodal data (text, images), and advanced optimization techniques to address challenges like data sparsity and cold-start problems. These advancements are improving the accuracy and efficiency of recommendation systems across diverse applications, from personalized content suggestions to anomaly detection in human behavior and even assisting in complex tasks like medical diagnosis. The field's impact stems from its ability to provide more accurate, efficient, and explainable recommendations in various domains.

Papers

October 28, 2024

Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training
Gustavo Escobedo, Christian Ganhör, Stefan Brandl, Mirjam Augstein, Markus Schedl
Adversarial Training Supervised Autoencoder Sensitive Attribute Latent Embeddings User Embeddings Variational AutoEncoder Neural Collaborative Filtering

September 27, 2024

Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories
Yueyang Liu, Lance Kennedy, Hossein Amiri, Andreas Züfle
Unlabeled Anomaly Trajectory Datasets Trajectory Data Mining Neural Collaborative Filtering Trajectory Anomaly Detection

July 25, 2024

Text-Driven Neural Collaborative Filtering Model for Paper Source Tracing
Aobo Xu, Bingyu Chang, Qingpeng Liu, Ling Jian
Recommendation System Source Tracing Neural Collaborative Filtering Reference Policy

June 17, 2024

Perceptron Collaborative Filtering
Arya Chakraborty
Machine Learning Model Recommender System Elementary Perceptron Neural Collaborative Filtering

May 28, 2024

Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop
Yuqi Zhou, Sunhao Dai, Liang Pang, Gang Wang, Zhenhua Dong, Jun Xu, Ji-Rong Wen
Raw Data Recommendation Model USER Provided Name Echo Chamber Source Bias Explicit Feedback Based Recommender System Neural Collaborative Filtering

November 15, 2023

User Persona Identification and New Service Adaptation Recommendation
Narges Tabari, Sandesh Swamy, Rashmi Gangadharaiah
Recommendation Model Item Based Collaborative Filtering Neural Collaborative Filtering User Trajectory User Persona

November 3, 2023

Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces
Aditya Desai, Benjamin Meisburger, Zichang Liu, Anshumali Shrivastava
Recommendation System Collaborative Filtering Recommendation Model Federated Averaging Heterogeneous Setting Neural Collaborative Filtering Random Subspace

August 30, 2023

Multimodal Recommender Systems in the Prediction of Disease Comorbidity
Aashish Cheruvu
Deep Learning Human Prediction Neural Collaborative Filtering Multimodal Recommender System

August 14, 2023

Cross-Attribute Matrix Factorization Model with Shared User Embedding
Wen Liang, Zeng Fan, Youzhi Liang, Jianguo Jia
Recommender System Matrix Factorization Multi User Neural Collaborative Filtering

August 8, 2023

RECipe: Does a Multi-Modal Recipe Knowledge Graph Fit a Multi-Purpose Recommendation System?
Ali Pesaranghader, Touqir Sajed
Complete Recipe Multi Modal Knowledge Graph Neural Collaborative Filtering Multi Scenario Recommendation

April 5, 2022

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
Jun Hu, Bryan Hooi, Shengsheng Qian, Quan Fang, Changsheng Xu
Graph Neural Network GNN Based Graph Diffusion Network Representation Item Embeddings Neural Collaborative Filtering Graph Collaborative Filtering

March 14, 2022

Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering
Ramin Raziperchikolaei, Young-joo Chung
Recommender System Many Parameter Rich Input Shallow Network Learnable Parameter Item Embeddings Simultaneous Learning Neural Collaborative Filtering

January 31, 2022

Neural Collaborative Filtering Bandits via Meta Learning
Yikun Ban, Yunzhe Qi, Tianxin Wei, Jingrui He
Personalized Recommendation Contextual Multi Armed Bandit Neural Collaborative Filtering

January 24, 2022

Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm
Wei Huang, Tianfu Qi, Yundi Guan, Qihang Peng, Jun Wang
Waveform Domain Neural Collaborative Filtering Intelligent Communication Waveform Design

Neural Collaborative Filtering

Papers

Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training

Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories

Text-Driven Neural Collaborative Filtering Model for Paper Source Tracing

Perceptron Collaborative Filtering

Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop

User Persona Identification and New Service Adaptation Recommendation

Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces

Multimodal Recommender Systems in the Prediction of Disease Comorbidity

Cross-Attribute Matrix Factorization Model with Shared User Embedding

RECipe: Does a Multi-Modal Recipe Knowledge Graph Fit a Multi-Purpose Recommendation System?

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering

Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering

Neural Collaborative Filtering Bandits via Meta Learning

Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm