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Collaborative filtering recommendation algorithm based on variational inference

Kai Zheng, Xianjun Yang, Yilei Wang, Yingjie Wu, Xianghan Zheng
2020 International Journal of Crowd Science  
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

Ahlem Drif, Houssem Eddine Zerrad, Hocine Cherifi
2020 IEEE Access  
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

Daeryong Kim, Bongwon Suh
2019 Proceedings of the 13th ACM Conference on Recommender Systems - RecSys '19  
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]

Ezgi Yıldırım, Payam Azad, Şule Gündüz Öğüdücü
2019 arXiv   pre-print
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]

Vojtěch Vančura, Pavel Kordík
2021 arXiv   pre-print
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

Yali Du, Meng Fang, Jinfeng Yi, Chang Xu, Jun Cheng, Dacheng Tao
2019 IEEE transactions on multimedia  
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

N.L. Anantha, Bhanu Bathula
2018 Advances in Modelling and Analysis B  
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]

Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez, Ángel González-Prieto
2021 arXiv   pre-print
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]

Robin Devooght, Hugues Bersini
2017 arXiv   pre-print
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]

Bahare Askari, Jaroslaw Szlichta, Amirali Salehi-Abari
2020 arXiv   pre-print
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]

Meimei Liu, Hongxia Yang
2020 arXiv   pre-print
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]

Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
2018 arXiv   pre-print
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

Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
2018 Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18  
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

Wei Zeng, Jiwei Qin, Chunting Wei
2021 IEEE Access  
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]

Qiaomin Yi, Ning Yang, Philip S. Yu
2021 arXiv   pre-print
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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