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A Review for Recommender System Models and Deep Learning

F. Nagy, A. Haroun, Hatem Abdel-Kader, Arabi Keshk
2021 IJCI. International Journal of Computers and Information  
traditional technology, how deep learning-based recommendation systems works, deep learning for recommendations and open problems and the novel research trends on this field.  ...  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  ...  collaborative filtering, Is a model for machine learning based family, based on the learned model by finding patterns in training data which is represented in complex rating the recommendation is made  ... 
doi:10.21608/ijci.2021.207864 fatcat:hdwzp3o4djcsdo6ubqfkdmu3o4

Deep Learning-Based Recommendation: Current Issues and Challenges

Rim Fakhfakh, Anis Ben, Chokri Ben
2017 International Journal of Advanced Computer Science and Applications  
The deep learning based recommender models provides a better detention of user preferences, item features and users-items interactions history.  ...  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  ...  Deep Collaborative Filtering Recommendation The Deep learning is applied for enhancing collaborative filtering based recommender system.  ... 
doi:10.14569/ijacsa.2017.081209 fatcat:lj5udwsfwfezxkwp5y7i44ruly

Improving Recommendation Techniques by Deep Learning and Large Scale Graph Partitioning

Gourav Bathla, Rinkle Rani, Himanshu Aggarwal
2018 International Journal of Advanced Computer Science and Applications  
Readers can identify best suitable model from these deep learning models for recommendation based on their needs and incorporate in their techniques.  ...  This can be removed by using deep learning models which can improve user-item matrix entries by using feature learning. In this paper, various models are explained with their applications.  ...  Deep learning enhances the improvement in input and output systems. Deep learning can be implemented for content based or collaborative filtering based or combination of different architectures.  ... 
doi:10.14569/ijacsa.2018.091049 fatcat:duhdn5ipknbflekirpkpnpvdhi

Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications

Bharadwaja Krishnadev Mylavarapu
2018 Journal of Computer and Communications  
Recommender system consists of several techniques for recommendations. Here we used the well known approach named as Collaborative filtering (CF).  ...  To make recommendation on items from the user for historical user rating several intelligent systems are using. The most common method is Recommendation systems.  ...  Mingsheng Fu, Hong Qu, Zhang Yi, Li Lu and Yongsheng Liu (2018) pro- posed a theory based on A Novel Deep Learning-Based CF Model for Recom- mendation Systems.  ... 
doi:10.4236/jcc.2018.612001 fatcat:bvcdfob5vjg2zgoavhrospblhe

A Survey of State-of-the-art: Deep Learning Methods on Recommender System

Basiliyos Tilahun, Charles Awono, Bernabe Batchakui
2017 International Journal of Computer Applications  
Due to the limitation of the traditional recommendation methods in obtaining accurate result a deep learning approach is introduced both for collaborative and content based approaches that will enable  ...  In this paper different traditional recommendation techniques, deep learning approaches for recommender system and survey of deep learning techniques on recommender system are presented.  ...  SURVEY OF DEEP LEARNING TECHNIQUES ON RECOMMENDER SYSTEM Deep learning has recently been proposed in building a recommender systems both for collaborative and content based approaches [10, 40] .  ... 
doi:10.5120/ijca2017913361 fatcat:txeaquy5dfdelezsly4g7ze3ca

Deep Edu: A Deep Neural Collaborative Filtering for Educational Services Recommendation

Farhan Ullah, Bofeng Zhang, Rehan Ullah Khan, Tae-Sun Chung, Muhammad Attique, Khalil Khan, Salim El Khediri, Sadeeq Jan
2020 IEEE Access  
To address these problems, we propose Deep Edu a novel Deep Neural Collaborative Filtering for educational services recommendation.  ...  INDEX TERMS Deep learning, services, deep neural collaborative filtering, educational, services recommendation.  ...  In this paper, we proposed the Deep Edu, a novel Deep Neural Collaborative Filtering for educational services recommendation.  ... 
doi:10.1109/access.2020.3002544 fatcat:soe2roqmcng5fo2t5icz2aftlq

"Major Challenges of Recommender System and Related Solutions"

Surya Naga Sai Lalitha Chirravuri, Kali Pradeep Immidi
2022 International Journal of Innovative Research in Computer Science & Technology  
Recommender system is a very young area of machine learning & Deep Learning research. The basic goal of the recommender system is to create a relationship between items and consumers.  ...  The relationship provides recommendations based on user interest. content-based, collaborative, demographic, hybrid filtering, knowledgebased, utility-based, classification model are well-known recommender  ...  Memory-based and Model-based techniques are the two most common forms of collaborative filtering systems.  ... 
doi:10.55524/ijircst.2022.10.2.3 fatcat:6dmvby2gujhkzh4swaji4qt5j4

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-learning Approaches

Sadaf Safavi, Mehrdad Jalali, Mahboobeh Houshmand
2022 Electronics  
Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work.  ...  it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses.  ...  POI Recommendation Systems Based on a Collaborative Filtering Method Collaborating Filtering is a technique that has often been used for recommender systems [93] [94] [95] .  ... 
doi:10.3390/electronics11131998 fatcat:exuhjcsn3rbw5d3xjsw3aykmhe


2020 Intelligent Data Analysis  
Their proposed content boosted hybrid filtering system provides a novel list of recommendations even for pessimistic users, with predictive accuracy better than that of a traditional content-based filtering  ...  a hybrid model based on deep learning and stacking integration strategy.  ... 
doi:10.3233/ida-200016 fatcat:qd22vaz7b5fehfy23sfcq6qtfi

On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract)

Vincent Francois-Lavet, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, Raphael Fonteneau
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
In the context of reinforcement learning with partial observability, this paper provides an analysis of the tradeoff between these two error sources.  ...  data) and a term due to overfitting (additional suboptimality due to limited data).  ...  Li et al. proposed a general deep collaborative filtering (DCF) framework, which unifies the deep learning models with matrix factorization based collaborative filtering [Li et al., 2015a] .  ... 
doi:10.24963/ijcai.2020/695 dblp:conf/ijcai/0001Z20 fatcat:yx2wihhuobgmjjh4aevkbr33g4

Novel Approach of Neural Collaborative Filter by Pairwise Objective Function with Matrix Factorization

Ram Sethuraman, Akshay Havalgi
2018 International Journal of Engineering & Technology  
The concept of deep learning is used in the various fields like text, speech and vision. The proposed work deep neural network is used for recommender system.  ...  The proposed framework is named as NCF which is basically neural network based collaborative filtering. The NCF gives the latent features by reducing the non-linearity and generalizing the matrix.  ...  Matrix factorization is a technique of collaborative filtering approach which learns a latent space to represent a user or an item becomes a standard model for recommendation due to its flexibility and  ... 
doi:10.14419/ijet.v7i3.12.17840 fatcat:xruysoytwjdildmgxicpuhjyim

A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering

Yu Liu, Shuai Wang, M. Shahrukh Khan, Jieyu He
2018 Big Data Mining and Analytics  
In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders (DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance  ...  However, there is as yet no research combining collaborative filtering and contentbased recommendation with deep learning.  ...  Fig. 1 1 Fig. 1 Deep hybrid recommender system based on autoencoders model. 1 W 1 Fig. 2 A four-layer stacked denoising auto-encoder.  ... 
doi:10.26599/bdma.2018.9020019 dblp:journals/bigdatama/LiuWKH18 fatcat:6qk3tn4dtjevxey2gg42ikzxaa

Integrating Stacked Sparse Auto-encoder into Matrix Factorization for Rating Prediction

Yihao Zhang, Chu Zhao, Mian Chen, Meng Yuan
2021 IEEE Access  
[23] employ autoencoder into recommender systems and propose an autoencoder based collaborative filtering model (ACF), while ACF fails to handle non-integer ratings. Strub et al.  ...  [25] design a novel method for top-N recommendation, called collaborative denoising auto-encoder model (CDAE), which is a neural network with single hidden layer. Yi et al.  ... 
doi:10.1109/access.2021.3053291 fatcat:bu4rse2porg5heelty67g2icxa

A Hybrid Recommender System for Recommending Smartphones to Prospective Customers [article]

Pratik K. Biswas, Songlin Liu
2021 arXiv   pre-print
In this paper, we propose a hybrid recommender system, which combines Alternative Least Squares (ALS) based collaborative filtering with deep learning to enhance recommendation performance as well as overcome  ...  Some hybrid recommender systems have combined collaborative filtering and content-based approaches to build systems that are more robust.  ...  In this paper, we have presented a novel architecture for a hybrid recommender system to address the gaps and improve the accuracy of a stand-alone collaborative filtering system through deep learning.  ... 
arXiv:2105.12876v1 fatcat:6q3wpnmkerd7pnnpsfhvbilxca

Representation Extraction and Deep Neural Recommendation for Collaborative Filtering [article]

Arash Khoeini, Saman Haratizadeh, Ehsan Hoseinzade
2020 arXiv   pre-print
In this paper, we investigate the usage of novel representation learning algorithms to extract users and items representations from rating matrix, and offer a deep neural network for Collaborative Filtering  ...  The resulted predictions are then used for the final recommendation.  ...  A recent approach in recommendation systems is deep Learning.  ... 
arXiv:2012.04979v1 fatcat:cefcugjfpndevijl6jnuvklaw4
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