A Generic Hybrid Recommender System based on Neural Networks

被引：0

作者：

Gupta, Anant ^{[1
]}

Tripathy, B. K. ^{[1
]}

机构：

[1] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore, Tamil Nadu, India

来源：

SOUVENIR OF THE 2014 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC) | 2014年

关键词：

Machine Learning; Recommender Systems; Neural Networks;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Content based recommender systems have the drawback of recommending only similar items to a user's particular taste, irrespective of the item's popularity. Collaborative Filtering based systems face the problem of data sparsity and expensive parameter training. In this paper, a combination of content-based, model and memory-based collaborative filtering techniques is used in order to remove these drawbacks and to present predicted ratings more accurately. The training of the data is done using feedforward backpropagation neural network and the system performance is analyzed under various circumstances like number of users, their ratings and system model.

引用

页码：1248 / 1252

页数：5

共 50 条

[41] A hybrid healthy diet recommender system based on machine learning techniques
Sweidan, Sara
Askar, S.S.
Abouhawwash, Mohamed
Badr, Elsayed
Computers in Biology and Medicine, 2025, 184
[42] Identifying movie genre compositions using neural networks and introducing GenRec - a recommender system based on audience genre perception
Pal, Aditya
Barigidad, Abhilash
Mustafi, Abhijit
PROCEEDINGS OF THE 2020 5TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND SECURITY (ICCCS-2020), 2020,
[43] An electronic performance support system based on a hybrid content-collaborative recommender system
Basile, Pierpaolo
de Gemmis, Marco
Gentile, Anna Lisa
Iaquinta, Leo
Lops, Pasquale
Semeraro, Giovanni
NEURAL NETWORK WORLD, 2007, 17 (06) : 529 - 541
[44] Toward a generic hybrid neural system for handwriting recognition: An application to Arabic words
LRI Laboratory, Department of Informatics, Badji Mokhtar University, BP 12, 23000, Annaba, Algeria
Neural Network World, 2006, 4 (327-340)
[45] Lithofacies classification based on a hybrid system of artificial neural networks and hidden Markov models
Feng, Runhai
GEOPHYSICAL JOURNAL INTERNATIONAL, 2020, 221 (03) : 1484 - 1498
[46] A Survey of Graph Neural Networks for Social Recommender Systems
Sharma, Kartik
Lee, Yeon-Chang
Nambi, Sivagami
Salian, Aditya
Shah, Shlok
Kim, Sang-Wook
Kumar, Srijan
ACM COMPUTING SURVEYS, 2024, 56 (10)
[47] Enhancing Matrix Factorization-based Recommender Systems via Graph Neural Networks
Guo, Zhiwei
Meng, Dian
Zhang, Huiyan
Wang, Heng
Yu, Keping
19TH IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2021), 2021, : 1053 - 1059
[48] Efficient Integration of Reinforcement Learning in Graph Neural Networks-Based Recommender Systems
Sharifbaev, Abdurakhmon
Mozikov, Mikhail
Zaynidinov, Hakimjon
Makarov, Ilya
IEEE ACCESS, 2024, 12 : 189439 - 189448
[49] Performance Analysis of Neural Networks-based Multi-criteria Recommender Systems
Hassan, Mohammed
Hamada, Mohamed
2017 2ND INTERNATIONAL CONFERENCES ON INFORMATION TECHNOLOGY, INFORMATION SYSTEMS AND ELECTRICAL ENGINEERING (ICITISEE): OPPORTUNITIES AND CHALLENGES ON BIG DATA FUTURE INNOVATION, 2017, : 490 - 494
[50] A High-Efficient Hybrid Physics-Informed Neural Networks Based on Convolutional Neural Network
Fang, Zhiwei
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (10) : 5514 - 5526

← 1 2 3 4 5 →