599 research outputs found
A Collective Variational Autoencoder for Top- Recommendation with Side Information
Recommender systems have been studied extensively due to their practical use
in many real-world scenarios. Despite this, generating effective
recommendations with sparse user ratings remains a challenge. Side information
associated with items has been widely utilized to address rating sparsity.
Existing recommendation models that use side information are linear and, hence,
have restricted expressiveness. Deep learning has been used to capture
non-linearities by learning deep item representations from side information but
as side information is high-dimensional existing deep models tend to have large
input dimensionality, which dominates their overall size. This makes them
difficult to train, especially with small numbers of inputs.
Rather than learning item representations, which is problematic with
high-dimensional side information, in this paper, we propose to learn feature
representation through deep learning from side information. Learning feature
representations, on the other hand, ensures a sufficient number of inputs to
train a deep network. To achieve this, we propose to simultaneously recover
user ratings and side information, by using a Variational Autoencoder (VAE).
Specifically, user ratings and side information are encoded and decoded
collectively through the same inference network and generation network. This is
possible as both user ratings and side information are data associated with
items. To account for the heterogeneity of user rating and side information,
the final layer of the generation network follows different distributions
depending on the type of information. The proposed model is easy to implement
and efficient to optimize and is shown to outperform state-of-the-art top-
recommendation methods that use side information.Comment: 7 pages, 3 figures, DLRS workshop 201
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์ตํฉ๊ณผํ๊ธฐ์ ๋ํ์ ์ตํฉ๊ณผํ๋ถ(๋์งํธ์ ๋ณด์ตํฉ์ ๊ณต),2019. 8. ์๋ด์.Since Matrix Factorization based linear models have been dominant in the Collaborative Filtering context for a long time in the past, Neural Network based CF Models for recommendation have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences and Variational Autoencoders where shown to give state-of-the-art results.
However, there are some potentially problematic characteristics of the current Variational Autoencoder for CF. The first is the too simplistic prior VAEs incorporate for learning the latent representations of user preference, which may be restricting the model from learning more expressive and richer latent variables that could boost recommendation performance. The other is the models inability to learn deeper representations with more than one hidden layer.
Our goal is to incorporate appropriate techniques in order to mitigate the aforementioned problems of Variational Autoencoder CF and further improve the recommendation performance of VAE based Collaborative Fil-tering. We bring the VampPrior, which successfully made improvements for image generation to tackle the restrictive prior problem. We also adopt Gat-ed Linear Units (GLUs) which were used in stacked convolutions for lan-guage modeling to control information flow in the easily deepening auto-encoder framework.
We show that such simple priors (in original VAEs) may be too restric-tive to fully model user preferences and setting a more flexible prior gives significant gains. We also show that VAMP priors coupled with gating mechanisms outperform SOTA results including the Variational Autoencoder for Collaborative Filtering by meaningful margins on 4 benchmark datasets (MovieLens, Netflix, Pinterest, Melon).์ต๊ทผ ๋ด๋ด๋ท ๊ธฐ๋ฐ ํ์
ํํฐ๋ง ์ถ์ฒ์๊ณ ๋ฆฌ์ฆ์ด ์ฃผ๋ชฉ์ ๋ฐ๊ณ ์๋ค. ๊ทธ ์ค ํ ๊ฐ๋์ ์ฐ๊ตฌ๋ ๊น์ ์์ฑ๋ชจํ (Deep Generative Model)์ ์ด์ฉํด ์ฌ์ฉ์๋ค์ ์ ํธ๋ฅผ ๋ชจ๋ธ๋งํ๋ ๋ฐฉ๋ฒ์ด๋ค. ์ด์ค Variational Autoencoder๋ฅผ (VAE) ์ด์ฉํ ๋ฐฉ๋ฒ์ด ์ต๊ทผ state-of-the-art (SOTA) ์ฑ๋ฅ์ ๋ณด์ฌ์ฃผ์๋ค. ๊ทธ๋ฌ๋ VAE๋ฅผ ์ด์ฉํ ํ์
ํํฐ๋ง ์๊ณ ๋ฆฌ์ฆ์ ํ์ฌ ๋ช ๊ฐ์ง์ ์ ์ฌ์ ์ธ ๋ฌธ์ ์ ๋ค์ ์ง๋๊ณ ์๋ค. ์ฒซ ๋ฒ์งธ๋ ์ฌ์ฉ์ ์ ํธ๋ฅผ ์์ถํ๋ ์ ์ฌ๋ณ์๋ฅผ ํ์ตํ๋ ๊ณผ์ ์์ ๋งค์ฐ ๋จ์ํ ์ฌ์ ๋ถํฌ๋ฅผ ์ฌ์ฉํ๋ค๋ ๊ฒ์ด๋ค. ๋ ๋ค๋ฅธ ๋ฌธ์ ์ ์ ๋ชจ๋ธ์ด ํ์ฌ ์ฌ๋ฌ ๋จ์ ์ด์ฉํ ๊น์ ์ธ์ฝ๋์ ๋์ฝ๋๋ฅผ ์ฌ์ฉํ์ง ๋ชปํ๊ณ ์๋ค๋ ๊ฒ์ด๋ค. ๋ณธ ์ฐ๊ตฌ๋ ์ต์ ๊ธฐ์ ๋ค์ ํ์ฉํ์ฌ ์์ ๋ฌธ์ ์ ๋ค์ ํด๊ฒฐํ๊ณ VAE๋ฅผ ์ด์ฉํ ํ์
ํํฐ๋ง ์๊ณ ๋ฆฌ์ฆ์ ์ถ์ฒ์ฑ๋ฅ์ ๋์ฑ ๋์ด๋ ๊ฒ์ด ๋ชฉํ์ด๋ค. ๋ณธ ์ฐ๊ตฌ๋ ํ์
ํํฐ๋ง ๋ฌธ์ ์ ๋ ๋ณต์กํ ์ฌ์ ๋ถํฌ (Flexible Prior)๋ฅผ ์ ์ฉํ ์ฒซ ์ฐ๊ตฌ๋ก์, ๊ธฐ์กด์ ๋จ์ํ ์ฌ์ ๋ถํฌ๊ฐ ๋ชจ๋ธ์ ํํ๋ ฅ์ ์ ํํ ์ ์์ผ๋ฉฐ ๋ ๋ณต์กํ ์ฌ์ ๋ถํฌ๋ฅผ ์ ์ํจ์ผ๋ก์จ ๋ชจ๋ธ์ ์ฑ๋ฅ์ ๋์ฑ ๋์ผ ์ ์์์ ๋ณด์๋ค. ์ด๋ฅผ ์ํด ์ด๋ฏธ์ง ์์ฑ ๋ฌธ์ ์์ ์ข์ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ธ VampPrior๋ฅผ ์ด์ฉํด ์คํ์ ์งํํ์๋ค. ๋ํ VampPrior๋ฅผ Gating Mechanisim๊ณผ ํจ๊ป ์ฌ์ฉํ์์ ๋ ๊ธฐ์กด SOTA๋ฅผ ๋์ด์๋ ์ฑ๋ฅ์ ๋ณด์์ ์ถ์ฒ์๊ณ ๋ฆฌ์ฆ์์ ์ฌ์ฉ๋๋ ๋ํ์ ์ธ ๋ฐ์ดํฐ์
๋ค์ ํตํด ๋ณด์ฌ์ค๋ค.1 INTRODUCTION 1
1.1 Background and Motivation 1
1.2 Research Goal 3
1.3 Enhancing VAEs for Collaborative Filtering 3
1.4 Experiments 5
1.5 Contributions 5
2 RELATED WORK 7
2.1 Collaborative Filtering 7
2.1.1 Traditional methods & Matrix-Factorization based CF 8
2.1.2 Autoencoders for CF 12
2.2 Deep Generative Models (VAE) 17
2.2.1 Variational Bayes 18
2.2.2 Variational Autoencoder 18
2.3 Variational Autoencoder for Collaborative Filtering 20
2.3.1 VAE for CF 21
2.4 Recent research in Computer Vision & Deep Learning 24
2.4.1 VampPrior 24
2.4.2 Gated CNN 25
3 METHOD 28
3.1 Flexible Prior 29
3.1.1 Motivation 29
3.1.2 VampPrior 30
3.1.3 Hierarchical Stochastic Units 31
3.2 Gating Mechanism 32
3.2.1 Motivation 32
3.2.2 Gated Linear Units 34
4 EXPERIMENT 35
4.1 Setup 35
4.1.1 Baseline Models 35
4.1.2 Proposed Models 37
4.1.3 Strong Generalization 37
4.1.4 Evaluation Metrics 38
4.2 Datasets 38
4.3 Configurations 39
4.4 Results 40
4.4.1 Model Performance 40
4.4.5 Further Analysis on the Effect of Gating 44
5 CONCLUSION 45
Bibliography 47
๊ตญ๋ฌธ์ด๋ก 51Maste
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