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Deep Item-based Collaborative Filtering for Top-N Recommendation [article]

Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong
2018 arXiv   pre-print
Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization.  ...  By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile.  ...  top-N item recommendation task.  ... 
arXiv:1811.04392v1 fatcat:lzlas6nslrhebkhvhuhow2mtze

Comparative study on traditional recommender systems and deep learning based recommender systems

N.L. Anantha, Bhanu Bathula
2018 Advances in Modelling and Analysis B  
In this paper performance of Traditional Recommender Systems and Deep Learning-based Recommender Systems are compared.  ...  Recommender systems is a big breakthrough for the field of e-commerce. Product recommendation is challenging task to e-commerce companies.  ...  recommendations offers both Collaborative filtering, Top N Recommendation.  ... 
doi:10.18280/ama_b.610202 fatcat:4iur3pjuujdkha6dyt3v6ntequ

DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network

Yan Xiao, Congdong Li, Vincenzo Liu
2022 Mathematics  
The DFM-GCN model consists of three parts: the left part DeepFM is used to capture the interactive information between the users and items; the deep neural network is used in the middle to model the left  ...  The prediction of the click-through rate (CTR) is critical in recommendation systems where the task is to estimate the probability that a user will click on a recommended item.  ...  Traditional recommendation systems include recommendation algorithms based on collaborative filtering [6, 7] (user-based collaborative filtering, content-based collaborative filtering) and hybrid recommendation  ... 
doi:10.3390/math10050721 fatcat:z3jylmbkgzfrziwf3zc5fcluci

Hybrid Recommender System Leveraging Stacked Convolutional Networks

Naresh Nelaturi, Dpt of Computer Science and Systems Engineering, Co llege of Engineering, Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India, G . Lavanya Devi, Dpt of Computer Science and Systems Engineering, Co llege of Engineering, Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India
2018 Journal of Engineering Science and Technology Review  
To attain the objective, this paper presented an approach, for generating efficient top-n recommendations using a hybrid recommender model.  ...  In specific, this paper proposes the idea of extracting knowledge for transfer learning leveraging pre-trained deep neural networks.  ...  Recommendation Process An item based top-N recommendation model is considered in our approach.  ... 
doi:10.25103/jestr.113.12 fatcat:2mxouuj2zfdjffk3nwb5lj4v4u

User Demographic Information and Deep Neural Network in Film Recommendation System based on Collaborative Filtering

Adrianus Lunardi Pradana, Computer Science Department, BINUS Graduate Program – Master of Computer Science Bina Nusantara University, Jakarta, Indonesia 11480, Antoni Wibowo
2022 International Journal of Emerging Technology and Advanced Engineering  
One of the major problems in deep neural network based collaborative filtering recommendation system was coldstart problem.  ...  Some recent work tried to improve model performance by modifying how the model modelled the interaction between user and item features to generate TOP-N recommendations.  ...  To provide a model for film recommendation system based on collaborative filtering that utilize user's demographic information and deep neural network. 2.  ... 
doi:10.46338/ijetae0522_16 fatcat:gfc5nm6cavgbhnb6xnujacgghm

Deep Collaborative Filtering: A Recommendation Method for Crowdfunding Project Based on the Integration of Deep Neural Network and Collaborative Filtering

Pei Yin, Jing Wang, Jun Zhao, Huan Wang, Hongcheng Gan, Wei Liu
2022 Mathematical Problems in Engineering  
collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation.  ...  In response to this phenomenon, this paper proposes a deep collaborative filtering algorithm.  ...  Memory-based collaborative filtering recommendations usually load data into memory for operations and generate recommendations based on similarity. ese include userbased CF and item-based CF collaborative  ... 
doi:10.1155/2022/4655030 fatcat:6nadmi32g5hrzaurn3bukqazzi

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.  ...  The more the top-n recommendations are similar to real top-n items ranked by the user, the NDCG gets closer to 1.  ... 
arXiv:2012.04979v1 fatcat:cefcugjfpndevijl6jnuvklaw4

Research on Recommendation Systems using Deep Learning Models

2019 International journal of recent technology and engineering  
Due to this scenario, recommender systems that can recommend items appropriate for user's interest and likings have become mandatory.  ...  Today, customers has the ability to purchase or sell different items with advancement of e-commerce website, nevertheless it made complicate to investigate the majority of appropriate items suitable for  ...  The Collaborative Filtering approach will forecast rating for item or predicts an ordered list of preferable top-K items.  ... 
doi:10.35940/ijrte.d4609.118419 fatcat:wvnmghk64zgltgutejx5bmolty

Collaborative filtering content for parental control in mobile application chatting

Muhamad Ridhwan Bin Mohamad Razali, Suzana Ahmad, Norizan Mat Diah
2019 Bulletin of Electrical Engineering and Informatics  
Implementation of the tool is by adapting Collaborative Filtering approach with User-Based model which focusing on recommendation on similar interest between users.  ...  Hence, in this paper, researchers present a tool that provides word recommendations for mobile application chatting.  ...  These N items will form the top N recommendations.  ... 
doi:10.11591/eei.v8i4.1634 fatcat:hxis7i5cznbw7mefu77bvukmcm

Deep Learning Architecture for Collaborative Filtering Recommender Systems

Jesus Bobadilla, Santiago Alonso, Antonio Hernando
2020 Applied Sciences  
This paper provides an innovative deep learning architecture to improve collaborative filtering results in recommender systems.  ...  The underlying idea is to recommend highly predicted items that also have been found as reliable ones.  ...  NCF: Neural Collaborative Filtering, RNCF: Reliability-based Neural Collaborative Filtering.  ... 
doi:10.3390/app10072441 fatcat:rahpirobifc2fiip7qv3ocgopy

An Explainable Autoencoder For Collaborative Filtering Recommendation [article]

Pegah Sagheb Haghighi, Olurotimi Seton, Olfa Nasraoui
2019 arXiv   pre-print
They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings.  ...  Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning.  ...  MEP@n = 1 |U| u ∈U |I exp ∩ I r ec | |I r ec | (9) where I exp is the set of explainable items for user u, I r ec is the set of items in the top-n recommendation list for user u, |I exp ∩ R r ec | is the  ... 
arXiv:2001.04344v1 fatcat:cwtrzis2v5c77g2p6c2wk2gv4y

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.  ...  set of N items that would be of interest to a certain user (top-N recommendation problem).  ... 
arXiv:2105.12876v1 fatcat:6q3wpnmkerd7pnnpsfhvbilxca

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  ...  A variety of techniques have been proposed to perform recommendation, including content based, collaborative and hybrid recommenders.  ...  Collaborative Deep Learning for Recommender Systems To address the cold start problem, [21] introduces Collaborative Deep Learning to utilize review texts and ratings. [21] Deep Content-based Music  ... 
doi:10.5120/ijca2017913361 fatcat:txeaquy5dfdelezsly4g7ze3ca

Recommendation with Multi-Source Heterogeneous Information

Li Gao, Hong Yang, Jia Wu, Chuan Zhou, Weixue Lu, Yue Hu
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source  ...  To this end, we in this paper consider item recommendations based on heterogeneous information sources.  ...  Acknowledgments We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported by the National Key  ... 
doi:10.24963/ijcai.2018/469 dblp:conf/ijcai/GaoYWZLH18 fatcat:63fvrois7ffsfk2ojwp3yeymly

Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback

Huazhen Liu, Wei Wang, Yihan Zhang, Renqian Gu, Yaqi Hao, Ahmed Mostafa Khalil
2022 Computational Intelligence and Neuroscience  
; a new loss function is designed based on explicit-implicit feedback to obtain the best parameters through the neural network training to predict the preference of users for items; finally, according  ...  Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system.  ...  preferences, but top-N based recommendation tasks, which recommend the top-N list of most interesting items for users based on their ranking of item preference predictions, use a ranking assessment compared  ... 
doi:10.1155/2022/9593957 pmid:35047036 pmcid:PMC8763527 fatcat:vwzwthw64jesrflazgxiy6gwta
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