Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Deep AutoEncoders for Collaborative Filtering   This paper proposes a novel model for the rating prediction task in
recommender systems which significantly outperforms previous state-of-the art
models on a time-split Netflix data set. Our model is based on deep autoencoder
with 6 layers and is trained end-to-end without any layer-wise pre-training. We
empirically demonstrate that: a) deep autoencoder models generalize much better
than the shallow ones, b) non-linear activation functions with negative parts
are crucial for training deep models, and c) heavy use of regularization
techniques such as dropout is necessary to prevent over-fiting. We also propose
a new training algorithm based on iterative output re-feeding to overcome
natural sparseness of collaborate filtering. The new algorithm significantly
speeds up training and improves model performance. Our code is available at
https://github.com/NVIDIA/DeepRecommender

2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th