Collaborative topic regression with denoising AutoEncoder for content and community co-representation
Personalized recommendation of items frequently faces scenarios where we have sparse observations on users' adoption of items. In the literature, there are two promising directions. One is to connect sparse items through similarity in content. The other is to connect sparse users through simila...
Saved in:
Main Authors: | NGUYEN, Trong T., LAUW, Hady W. |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2017
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/3883 https://ink.library.smu.edu.sg/context/sis_research/article/4885/viewcontent/CollaborativeTopicRegression_AutoEncoder_2017.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Bilateral variational autoencoder for collaborative filtering
by: TRUONG, Quoc Tuan, et al.
Published: (2021) -
Attentive Group Recommendation
by: Da Cao, et al.
Published: (2020) -
Addressing cold-start in app recommendation: Latent user models constructed from twitter followers
by: Lin, J., et al.
Published: (2014) -
Towards source-aligned variational models for cross-domain recommendation
by: SALAH, Aghiles, et al.
Published: (2021) -
Collaborative topic regression for online recommender systems: An online and Bayesian approach
by: LIU, Chenghao, et al.
Published: (2017)