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Filters
Collaborative filtering recommendation algorithm based on variational inference
2020
International Journal of Crowd Science
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Therefore, using variational inference for collaborative filtering recommendation is of practical value. ...
Originality/value This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic ...
Take x and b as inputs, output x', and further use x' for Top-N recommendations.
3.2.3 Using free bits to optimize collaborative filtering recommendations. ...
doi:10.1108/ijcs-10-2019-0030
fatcat:ulpjrrifbjhdhktuq2nifra4sq
EnsVAE: Ensemble Variational Autoencoders for Recommendations
2020
IEEE Access
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learning techniques to extract features from the item-based side information. 2-) a variant of neural collaborative filtering recommender, named "Gate Recurrent Unit-based Matrix Factorization recommender ...
Hence, The Ensemble Variational Autoencoder framework for recommendations (EnsVAE) is proposed. ...
. • Variational Autoencoders for Collaborative Filtering: [23] This technique extends variational autoencoders (VAEs) to collaborative filtering for implicit feedback. ...
doi:10.1109/access.2020.3030693
fatcat:wijvecqbczecrm52jlffv6q5ey
Enhancing VAEs for collaborative filtering
2019
Proceedings of the 13th ACM Conference on Recommender Systems - RecSys '19
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2019 pre-print arXiv:1911.00936v1 fatcat:u6q3ufnvujdmre54l62mt6ojqy
Neural network based models for collaborative filtering have started to gain attention recently. ...
We also show that VampPriors coupled with gating mechanisms outperform SOTA results including the Variational Autoencoder for Collaborative Filtering by meaningful margins on 2 popular benchmark datasets ...
] and Neural Collaborative Filtering (NCF) [9] . ...
doi:10.1145/3298689.3347015
dblp:conf/recsys/KimS19
fatcat:evkubhaurjhhnlcqr22psgcjcy
Neural Hybrid Recommender: Recommendation needs collaboration
[article]
2019
arXiv
pre-print
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This framework named NHR, short for Neural Hybrid Recommender allows us to include more elaborate information from the same and different data sources. ...
In this paper, we introduce a generalized neural network-based recommender framework that is easily extendable by additional networks. ...
The authors would like to thank Istanbul Technical University for their financial support under the project BAP-40737. ...
arXiv:1909.13330v1
fatcat:a3z2hdzdovgfnmaebovus5ymui
Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
[article]
2021
arXiv
pre-print
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Recently introduced EASE algorithm presents a simple and elegant way, how to solve the top-N recommendation task. ...
Also, there is a growing interest in the recsys community to utilize variational autoencoders (VAE) for this task. ...
Recall for TOP-k recommended items, denoted Recall@k is defined as Recall@k = |R k ∩ R k | R k (16) whereR k is top-k items recommended by evaluated model.
C. ...
arXiv:2102.05774v1
fatcat:ooytegm2l5hw7jprqrefedsi6q
Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks
2019
IEEE transactions on multimedia
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Recent collaborative filtering methods based on the deep neural network are studied and introduce promising results due to their power in learning hidden representations for users and items. ...
collaborative filtering model. ...
For neural collaborative filtering system, we have two inputs from users and items. ...
doi:10.1109/tmm.2018.2887018
fatcat:ulpj5njdindjrbrqvhqsb2v4fy
Comparative study on traditional recommender systems and deep learning based recommender systems
2018
Advances in Modelling and Analysis B
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Recommender systems is a big breakthrough for the field of e-commerce. Product recommendation is challenging task to e-commerce companies. ...
In this paper performance of Traditional Recommender Systems and Deep Learning-based Recommender Systems are compared. ...
recommendations offers both Collaborative filtering, Top N Recommendation. ...
doi:10.18280/ama_b.610202
fatcat:4iur3pjuujdkha6dyt3v6ntequ
Deep Variational Models for Collaborative Filtering-based Recommender Systems
[article]
2021
arXiv
pre-print
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Deep learning provides accurate collaborative filtering models to improve recommender system results. ...
Our proposed models apply the variational concept to inject stochasticity in the latent space of the deep architecture, introducing the variational technique in the neural collaborative filtering field ...
https://github.com/KNODIS-Research-Group/deep-variational-models-for-collaborative-filtering 2 www.kaggle.com ...
arXiv:2107.12677v1
fatcat:eizxftcabzfirp66tgphrl7oai
Collaborative Filtering with Recurrent Neural Networks
[article]
2017
arXiv
pre-print
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We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. ...
In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation ...
We explore a new approach to collaborative filtering, based on recurrent neural networks. ...
arXiv:1608.07400v2
fatcat:np7saeu3sjcurm7vq3bme3v4m4
Joint Variational Autoencoders for Recommendation with Implicit Feedback
[article]
2020
arXiv
pre-print
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By extending the objective function of JoVA with a hinge-based pairwise loss function (JoVA-Hinge), we further specialize it for top-k recommendation with implicit feedback. ...
Variational Autoencoders (VAEs) have recently shown promising performance in collaborative filtering with implicit feedback. ...
Mult-VAE (Liang et al. 2018 ) is a collaborative filtering model for implicit feedback based on variational autoencoders. ...
arXiv:2008.07577v1
fatcat:imcjmb4kmza7fbrt2w7iukkrx4
DVE: Dynamic Variational Embeddings with Applications in Recommender Systems
[article]
2020
arXiv
pre-print
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We further apply DVE to sequence-aware recommender systems, and develop an end-to-end neural architecture for link prediction. ...
In this paper, we introduce a dynamic variational embedding (DVE) approach for sequence-aware data based on recent advances in recurrent neural networks. ...
The evaluation is done on top-k recommendation. The performance of a ranked list is judged by the overall top-k Hit Ratio (HR@k) and overall top-k Normalized Discounted Cumulative Gain (NDCG@k). ...
arXiv:2009.08962v1
fatcat:wwj3d7m46jeofojve52537vssy
Variational Autoencoders for Collaborative Filtering
[article]
2018
arXiv
pre-print
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We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. ...
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.We introduce a generative ...
Neural networks for collaborative filtering. ...
arXiv:1802.05814v1
fatcat:qtdx2jcdfvdbjmfdtprcjxwasi
Variational Autoencoders for Collaborative Filtering
2018
Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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We extend variational autoencoders (vaes) to collaborative filtering for implicit feedback. ...
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. ...
Neural networks for collaborative filtering. ...
doi:10.1145/3178876.3186150
dblp:conf/www/LiangKHJ18
fatcat:baidkwo2kvaldh3mr4meqlbxaa
Neural Collaborative Autoencoder for Recommendation with Co-occurrence Embedding
2021
IEEE Access
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matrix embedding part; (2) the collaborative neural recommendation part. ...
Collaborative filtering is the one of the most successful methods used by recommendation system to solve the information overload problem. ...
EXPERIMENTAL RESULTS Top-K Item Recommendation Result. Figure 3 show the top-k recommendation performance of different method on the AMusic dataset. ...
doi:10.1109/access.2021.3133628
fatcat:orzxvqybzvdennee63trnhb2ey
Dual Adversarial Variational Embedding for Robust Recommendation
[article]
2021
arXiv
pre-print
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In this paper, we propose a novel model called Dual Adversarial Variational Embedding (DAVE) for robust recommendation, which can provide personalized noise reduction for different users and items, and ...
Robust recommendation aims at capturing true preference of users from noisy data, for which there are two lines of methods have been proposed. ...
• CDAE [30]: CADE is a Denoising Auto-encoder
based collaborative filtering framework for top-k
recommendation. ...
arXiv:2106.15779v1
fatcat:3776g6w36rfjnnsmhpu3dgfuha
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