Deep heterogeneous autoencoders for Collaborative Filtering

Deep heterogeneous autoencoders for Collaborative Filtering

This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems. We propose a model that learns a shared feature space from heterogeneous data, such as item descriptions, product tags and online purchase history, to obtain better predictions. Our...

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Bibliographic Details
Main Authors: Li, Tianyu, Ma, Yukun, Xu, Jiu, Stenger, Björn, Liu, Chen, Hirate, Yu
Other Authors: School of Computer Science and Engineering
Format: Conference Paper
Language:English
Published: 2020
Subjects:
Engineering::Computer science and engineering
Deep Autoencoder
Heterogeneous Data
Online Access:https://hdl.handle.net/10356/144026

Internet

https://hdl.handle.net/10356/144026

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