The first paper on application with AE on Recsys, maybe.
He said this is for CF. (I think it ---)
Number of estimation parameter: 2mk + m + k ???(do calculate)
BG
MF is non-efficient computation using both item and user interaction to decompose Matrix into each latent reprentitve vecter.
Approach in this work, Model
Using simple AE but differing from use of visible input and regularization.
Common recomendation domain(and Scenario) Ml and Netflix
Item (or user) based-Recommendation
Experiment setting & Evaluation
Dataset
Movielens(1M small, 10M middle size),
Netflix()
As competitive models, BMF, CF-RBM() and LLORMF,
Metric
RMSE
Tunning
Hyperparameter
unit_size,
hidden layer number,
activation function
Setting
Use ONLY 3 STAR (for equal?paradigm)
Result
Outperformed item-based AE than user-based one as (rich data? less bias?)
Discuusion
Why? ()
What you should do next
AE for mechinsm and find improvement (Bailu? 2012? totu, AE)
VAE tuto, and appied to Rec.
-- not gaussian dist.
Ref
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. AutoRec: Autoencoders Meet Collaborative Filtering. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 111-112. DOI: http://dx.doi.org/10.1145/2740908.2742726
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