DeepAI AI Chat
Log In Sign Up

Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering

03/14/2022
by   Ramin Raziperchikolaei, et al.
0

Neural network-based collaborative filtering systems focus on designing network architectures to learn better representations while fixing the input to the user/item interaction vectors and/or ID. In this paper, we first show that the non-zero elements of the inputs are learnable parameters that determine the weights in combining the user/item embeddings, and fixing them limits the power of the models in learning the representations. Then, we propose to learn the value of the non-zero elements of the inputs jointly with the neural network parameters. We analyze the model complexity and the empirical risk of our approach and prove that learning the input leads to a better generalization bound. Our experiments on several real-world datasets show that our method outperforms the state-of-the-art methods, even using shallow network structures with a smaller number of layers and parameters.

READ FULL TEXT
08/16/2017

Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on sp...
06/26/2019

Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering

As the core of recommender system, collaborative filtering (CF) models t...
08/18/2020

Learning the Structure of Auto-Encoding Recommenders

Autoencoder recommenders have recently shown state-of-the-art performanc...
05/09/2019

Compositional Coding for Collaborative Filtering

Efficiency is crucial to the online recommender systems. Representing us...
05/16/2020

Neural Collaborative Reasoning

Collaborative Filtering (CF) has been an important approach to recommend...
08/13/2020

Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography

The segmentation of the mitral valve annulus and leaflets specifies a cr...
01/27/2018

Interactive Deep Colorization With Simultaneous Global and Local Inputs

Colorization methods using deep neural networks have become a recent tre...