Multi-model deep learning approach for collaborative filtering recommendation system

Multi-model deep learning approach for collaborative filtering recommendation system

As a result of a huge volume of implicit feedback such as browsing and clicks, many researchers are involving in designing recommender systems (RSs) based on implicit feedback. Though implicit feedback is too challenging, it is highly applicable to use in building recommendation systems. Conventiona...

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Bibliographic Details
Main Authors: Mohammed Fadhel Aljunid, Manjaiah Doddaghatta Huchaiah
Format: Article
Language:English
Published: Wiley 2020-11-01
Series:CAAI Transactions on Intelligence Technology
Subjects:
recommender systems
collaborative filtering
learning (artificial intelligence)
neural nets
matrix decomposition
conventional collaborative filtering techniques
user preferences
item latent features
deep neural networks
maps user
item features
multimodel deep learning approach
item functions
mmdl approach
deep autoencoder
one-dimensional convolution neural network model
collaborative filtering recommendation system
implicit feedback
Online Access:https://digital-library.theiet.org/content/journals/10.1049/trit.2020.0031

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https://digital-library.theiet.org/content/journals/10.1049/trit.2020.0031

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