• Corpus ID: 216080737

Conditioned Variational Autoencoder for top-N item recommendation

@article{Polato2020ConditionedVA,
  title={Conditioned Variational Autoencoder for top-N item recommendation},
  author={Mirko Polato and Tommaso Carraro and Fabio Aiolli},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.11141}
}
In this paper, we propose a Conditioned Variational Autoencoder (C-VAE) for constrained top-N item recommendation where the recommended items must satisfy a given condition. The proposed model architecture is similar to a standard VAE in which the condition vector is fed into the encoder. The constrained ranking is learned during training thanks to a new reconstruction loss that takes the input condition into account. We show that our model generalizes the state-of-the-art Mult-VAE… 
View With Semantic Reader
2 Citations
Background Citations
1
Methods Citations
1

Figures and Tables from this paper

2 Citations

Citation Type

Citation Type

A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering
TLDR
An intuitive interpretation of the inner representation of a conditioned variational autoencoder (C-VAE) for collaborative filtering and shows that in the latent space conditions on correlated genres map users in close clusters enables the model to be used for profiling purposes.
Implicit relation-aware social recommendation with variational auto-encoder
TLDR
ITRA could provide recommendations by reconstructing a non-binary adjacency social matrix with implicit high-order interactions from both indirect key opinion leaders and explicit connections from neighbors, and could achieve a better performance compared to the state-of-the-art methods.

References

SHOWING 1-10 OF 30 REFERENCES
Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
TLDR
It is demonstrated that the proposed model is a generalization of several well-known collaborative filtering models but with more flexible components, and that CDAE consistently outperforms state-of-the-art top-N recommendation methods on a variety of common evaluation metrics.
Style conditioned recommendations
TLDR
The main contribution is an application of a semi-supervised approach that extends item labels to interpretable user profiles that allows style injection to diversify recommendations while maintaining personal relevance.
Variational Autoencoders for Collaborative Filtering
TLDR
A generative model with multinomial likelihood and use Bayesian inference for parameter estimation is introduced and the pros and cons of employing a principledBayesian inference approach are identified and characterize settings where it provides the most significant improvements.
Collaborative Variational Autoencoder for Recommender Systems
TLDR
A Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario that is able to significantly outperform the state-of-the-art recommendation methods with more robust performance is proposed.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial…
Boolean kernels for collaborative filtering in top-N item recommendation
Conditional Variational Autoencoder for Neural Machine Translation
TLDR
This is the first reported conditional variational model for text that meaningfully utilizes the latent variable without weakening the translation model, and is extended with a co-attention mechanism motivated by Parikh et al. in the inference network.
Visually-Aware Fashion Recommendation and Design with Generative Image Models
TLDR
The model is able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pretrained visual features, and can be used generatively, i.e., used to suggest styles and aid the design of new products.
Embarrassingly Shallow Autoencoders for Sparse Data
TLDR
A linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems, is defined, and it is shown that its training objective has a closed-form solution, and the resulting conceptual insights are discussed.
Neural Collaborative Filtering
TLDR
This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.
...
...