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Filters
Neural Collaborative with Sentence BERT for News Recommender System
2021
JOIV: International Journal on Informatics Visualization
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Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks. ...
The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering. ...
Content-based filtering provides new recommendations based on content in providing recommendations. For example, there are titles, categories, and news content in the news. ...
doi:10.30630/joiv.5.4.678
fatcat:gaboovym3rax3h5js2cod25nxa
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. ...
Recommender Systems are categorized into mainly 3 types 1) Content based filtering 2) Collaborative filtering 3) Knowledge-based Systems. The content in Content Based filtering means descriptions. ...
doi:10.18280/ama_b.610202
fatcat:4iur3pjuujdkha6dyt3v6ntequ
Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works
2017
International Journal of Computer Applications
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for recommendation. ...
We organize the review in three parts: Collaborative system, Content based system and Hybrid system. ...
This stage of news recommendation is a step before final news recommendation for end users. ...
doi:10.5120/ijca2017916055
fatcat:m6icpquumbgczhrdnya7x35of4
Deep Learning-Based Recommendation: Current Issues and Challenges
2017
International Journal of Advanced Computer Science and Applications
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Then we finish by presenting the recommendation approach adopted by the most popular video recommendation platform YouTube which is based essentially on deep learning advances. ...
In this paper, we provide a recent literature review of researches dealing with deep learning based recommendation approaches which is preceded by a presentation of the main lines of the recommendation ...
Challenges and Current Issues The collaborative filtering and the content based filtering models are commonly used for recommendation. ...
doi:10.14569/ijacsa.2017.081209
fatcat:lj5udwsfwfezxkwp5y7i44ruly
MovieANN: Film Öneri Sistemlerine Çok Katmanlı Yapay Sinir Ağı Kullanarak Karma Bir Yaklaşım
2019
Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi
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These systems are based on collaborative filtering, content based filtering and hybrid approaches. ...
We combined collaborative and contentbased filtering to build a hybrid movie recommendation system, MovieANN, based on neural network model. ...
Collaborative filtering generalizes the preferences of the similar users. Thus, recommendation is easy for a new user when compared to content based filtering. ...
doi:10.28979/comufbed.597093
fatcat:bxsczv3elbfdbj5i2hwiux4t54
User Demographic Information and Deep Neural Network in Film Recommendation System based on Collaborative Filtering
2022
International Journal of Emerging Technology and Advanced Engineering
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Keywords — collaborative filtering, deep neural network, demographic information, neural collaborative filtering, recommendation system ...
One of the major problems in deep neural network based collaborative filtering recommendation system was coldstart problem. ...
Other research shows that neural collaborative filtering can also be used for news recommender system with additional help from sentence BERT [15] and gender debiased career and college major recommendation ...
doi:10.46338/ijetae0522_16
fatcat:gfc5nm6cavgbhnb6xnujacgghm
A Study of Neural Network Learning-Based Recommender System
2017
The International Journal of Engineering and Science
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This study proposes a neural network learning model as a new technique to find neighboring users using the collaborative filtering method. ...
In particular, collaborative filtering (CF) is the most widely used technique in these recommendation systems. ...
Recommendation techniques include collaborative filtering, content-based filtering and demographic filtering. ...
doi:10.9790/1813-0601038486
fatcat:qq3xupqpmvewbarzoadyv2yl4q
A Hybrid Latent Variable Neural Network Model for Item Recommendation
[article]
2014
arXiv
pre-print
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LNN outperforms a broad selection of content-based filters (which make recommendations based on item descriptions) and other hybrid approaches while maintaining the accuracy of state-of-the-art collaborative ...
In this paper, we present a neural network model with latent input variables (latent neural network or LNN) as a hybrid collaborative filtering technique that addresses the cold-start problem. ...
Two commonly used classes of recommender systems are content-based filters and collaborative filters. ...
arXiv:1406.2235v1
fatcat:mocr4cycvbc4vggqyerfp36cra
Content-Aware Collaborative Music Recommendation Using Pre-Trained Neural Networks
2015
Zenodo
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ACKNOWLEDGEMENTS We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.
REFERENCES [1] Thierry Bertin-Mahieux, Daniel P.W. ...
CONCLUSION In this paper we present a content-aware collaborative music recommendation system that joins a multi-layer neural network content model with a collaborative filtering model. ...
Two primary approaches exist in recommendation: collaborative filtering and content-based methods. ...
doi:10.5281/zenodo.1416308
fatcat:grpfm3xuefhllhc5lv6kmpxdly
An Application-oriented Review of Deep Learning in Recommender Systems
2019
International Journal of Intelligent Systems and Applications
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Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years. ...
This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications. ...
Traditional algorithms for designing RSs are mainly of three types: collaborative filtering, content-based filtering and hybrid algorithms. ...
doi:10.5815/ijisa.2019.05.06
fatcat:67fgexfbfjh2no5b3phvohbole
Content Filtering Enriched GNN Framework for News Recommendation
[article]
2021
arXiv
pre-print
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It is compatible with existing GNN-based approaches for news recommendation and can capture both collaborative and content filtering information simultaneously. ...
In this paper, to address such limitations, we propose content filtering enriched GNN framework for news recommendation, ConFRec in short. ...
The proposed framework improves the recommendation performance by fully considering both collaborative and content filtering information, and is compatible with existing GNN-based approaches for news recommendation ...
arXiv:2110.12681v1
fatcat:ryu33qdjwvhg3jn7s5rnq4eyt4
A Hybrid Recommender System for Recommending Smartphones to Prospective Customers
[article]
2021
arXiv
pre-print
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Some hybrid recommender systems have combined collaborative filtering and content-based approaches to build systems that are more robust. ...
In essence, we use the outputs from ALS (collaborative filtering) to influence the recommendations from a Deep Neural Network (DNN), which combines characteristic, contextual, structural and sequential ...
A variety of techniques has been proposed over the years for performing recommendations, including collaborative filtering, content-based filtering, demographic filtering, etc. ...
arXiv:2105.12876v1
fatcat:6q3wpnmkerd7pnnpsfhvbilxca
Deep Learning Innovations in Recommender Systems
2019
International Journal of Computer Applications
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These deep recommender systems can be used to understand the demands of users and improve the value in recommendations. ...
In this study we provide an overview of traditional approaches their limitations and then discuss about the aspects of deep learning used in the recommender system domain to improve the accuracy in recommender ...
A typical scenario is to apply content based statistics of a new item without the user rate in the collaborative filtering recommender systems [5] . ...
doi:10.5120/ijca2019918882
fatcat:tuhfyyytkzbifc74ewqjgcs5wq
A Review for Recommender System Models and Deep Learning
2021
IJCI. International Journal of Computers and Information
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In this paper we introduce an overview for the traditional recommendation systems models, the recommendation systems advantages and shortcoming, the recommendation systems challenges, common deep learning ...
traditional technology, how deep learning-based recommendation systems works, deep learning for recommendations and open problems and the novel research trends on this field. ...
Collaborative Filtering
Content Based filtering
Hybrid filtering
Number of users VI. ...
doi:10.21608/ijci.2021.207864
fatcat:hdwzp3o4djcsdo6ubqfkdmu3o4
Improvement of e-commerce recommendation systems with deep hybrid collaborative filtering with content: A case study
2020
Econometrics
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This paper presents a proposition to utilize flexible neural network architecture called Deep Hybrid Collaborative Filtering with Content (DHCF) as a product recommendation engine. ...
The system was tested on 2018 Amazon Reviews Dataset, using repeated cross validation and compared with other approaches: collaborative filtering (CF) and deep collaborative filtering (DCF) in terms of ...
As technology and processing power evolved, new ideas for solving the problem emerged -ranging from matrix decomposition, through collaborative filtering, to neural networks. ...
doi:10.15611/eada.2020.3.03
fatcat:efkdsujufnbrjbinns7oe4cxt4
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