research-article

A Comprehensive Collaborative Filtering Approach using Autoencoder in Recommender System

Authors:

Mahamudul Hasan,

Md. Tasdikul Hasan,

Md. Selim Reza,

Md. Nirab Akonda,

M. Saddam Hossain Khan,

Md. Mohsin UddinAuthors Info & Claims

ICCAI '19: Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence

Pages 185 - 189

https://doi.org/10.1145/3330482.3330518

Published: 19 April 2019 Publication History

Abstract

Recommender System is such kind of method where a user can get a recommendation by analyzing the user's previous preferences or behavior. It is an approach which helps a user to find items and contents by predicting their rating and showing them the recommended products. Items in the user based model are recommended to a user based on his/her similar user's preferences. In this paper, a strategy has been proposed in which calculation of the similarity between users have been done by using Autoencoder (AE) feature on the movielens data set. By using the autoencoder user-features have been calculated. Two users might like the same type of products and might give the same ratings to common products, so their features should be identical. By taking this relevant information into account, the similarity between users has been estimated and a collaborative filtering system based on user has been proposed. A visualization of the results of the recommendation system after using the evaluation methods have also been provided.

References

[1]

Mohammad Yahya H Al-Shamri. 2016. User profiling approaches for demographic recommender systems. Knowledge-Based Systems 100 (2016), 175--187.

Digital Library

[2]

CÃı'dric Haverkamp Karl D. Rann Bowman, David MJS and Lynda D. Prior. 2018. Differential demographic filtering by surface fires: How fuel type and fuel load affect sapling mortality of an obligate seeder savanna tree. Journal of Ecology 106, 3 (2018), 1010--1022.

[3]

Ho-fung Leung Qing Li Huaqing Min Jie Tang Cai, Yi and Juanzi Li. 2014. Typicality-based collaborative filtering recommendation. IEEE Transactions on Knowledge and Data Engineering 26, 3 (2014), 766--779.

Digital Library

[4]

Xiaoxue Zhao Dell Zhang, JunWang. 2015. Estimating the Uncertainty of Average F1 Scores. In Proceedings of the 2015 International Conference on The Theory of Information Retrieval. ACM, 317--320.

Digital Library

[5]

William A. Landi Giang, Phan H. and Sathyakama Sandilya. 2016. System and method for data sensitive filtering of patient demographic record queries. 9 (2016), 283.

[6]

Ivo Danihelka Alex Graves Danilo Jimenez Rezende Gregor, Karol and Daan Wierstra. 2015. Draw: A recurrent neural network for image generation. arXiv preprint arXiv:1404.2188.

[7]

Mohammad Amin Sobati Heidari, Elham and Salman Movahedirad. 2016. Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemometrics and intelligent laboratory systems 155 (2016), 73--85.

[8]

Andrew Howard. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. In Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D.,Wang,W.,Weyand, T., An-dreetto, M. and Adam, H. arXiv preprint arXiv:1404.2188.

[9]

Aixin Sun Hu, Longke and Yong Liu. 2014. Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction. In Proceedings of the 37th international ACM SIGIR conference on Research and development in information retrieval. ACM, 345--354.

Digital Library

[10]

Timm LinderWerner Gaulke Hussein, Tim and JÃijrgen Ziegler. 2014. Hybreed: A software framework for developing context-aware hybrid recommender systems. UserModeling and User-Adapted Interaction. UserModeling and User-Adapted Interaction 24, 1-2 (2014), 121--174.

Digital Library

[11]

Ojokoh BA Isinkaye FO, Folajimi YO. 2015. Recommendation systems:Principles, methods and evaluation. Egyptian Informatics Journal 16, 3 (2015), 261--273.

[12]

Edward Grefenstette Kalchbrenner, Nal and Phil Blunsom. 2014. A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188.

[13]

Y. Koren and R Bell. 2015. Advances in collaborative filtering. In Recommender systems handbook. Springer.

Digital Library

[14]

Shobeir Fakhraei James Foulds Magdalini Eirinaki Kouki, Pigi and Lise Getoor. 2015. Hyper: A flexible and extensible probabilistic framework for hybrid recommender systems. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 99--106.

Digital Library

[15]

Georgia Koutrika. 2018. Modern Recommender Systems: from Computing Matrices to Thinking with Neurons. In Proceedings of the 2018 International Conference on Management of Data. ACM, 1651--1654.

Digital Library

[16]

Zheng Hu Ahmad Mian Hui Tian Liu, Haifeng and Xuzhen Zhu. 2014. A new user similarity model to improve the accuracy of collaborative filtering. Knowledge- Based Systems 56 (2014), 156--166.

Digital Library

[17]

Md Ariful Islam Malik Shabbir Ahmed Mahamudul Hasan, Shibbir Ahmed. 2016. A comprehensive approach towards user-based collaborative filtering recommender system. In Proceedings of the 2016 International Workshop on Computational Intelligence (IWCI). IEEE, 159--164.

[18]

Nomusa Dlodlo Maravanyika, Munyaradzi and Nobert Jere. 2017. An adaptive recommender-system based framework for personalised teaching and learning on e-learning platforms. In ST-Africa Week Conference (IST-Africa). IEEE, 1--9.

[19]

Michael J. Taylor Marc A. Najork, Hugo Zaragoza. 2007. Hits on the web: how does it compare?. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 471--478.

Digital Library

[20]

Harston C.T. Maren, A.J. and R.M Pap. 2014. Handbook of neural computing applications. Academic Press.

[21]

Jonathan L. Herlocker Matthew R. McLaughlin. 2004. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 329--336.

Digital Library

[22]

Lior Rokach Ricci, Francesco and Bracha Shapira. 2015. Recommender systems: introduction and challenges. Springer.

[23]

Eneldo Loza MencÃ&eng;a Ristoski, Petar and Heiko Paulheim. 2014. A hybrid multi-strategy recommender system using linked open data. In Semantic Web Evaluation Challenge (2014), 150--156.

[24]

JÃijrgen Schmidhuber. 2015. Deep learning in neural networks: An overview. In Neuralnetworks 61. Elsevier, 85--117.

Digital Library

[25]

Romaric Gaudel Strub, Florian and JÃı'rÃı'mie Mary. 2016. Hybrid recommender system based on autoencoder. In Proceedings of the 1stWorkshop on Deep Learning for Recommender Systems. ACM, 11--16.

Digital Library

[26]

Chenwei Deng Tang, Jiexiong and Guang-Bin Huang. 2016. Extreme learning machine for multilayerperceptron. IEEE transactions on neural networks and learning systems 27, 4 (2016), 809--821.

[27]

Naiyan Wang Wang, Hao and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1235--1244.

Digital Library

[28]

Wenjun Wang Xinyu Zhou Hui Sun Jia Zhao Xiang Yu Wang, Hui and Zhihua Cui. 2017. Firefly algorithm with neighborhood attraction. Information Sciences 382 (2017), 374--387.

Digital Library

[29]

Ilya Sutskever Zaremba, Wojciech and Oriol Vinyals. 2014. Recurrent neural network regularization. arXiv preprint arXiv:1404.2188

Cited By

Tian BLiu SFang J(2023)A Novel Approach with Multi-view Features for Recommendation2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)10.1109/ITAIC58329.2023.10409063(473-477)Online publication date: 8-Dec-2023
https://doi.org/10.1109/ITAIC58329.2023.10409063
Thakker UPatel RShah M(2021)A comprehensive analysis on movie recommendation system employing collaborative filteringMultimedia Tools and Applications10.1007/s11042-021-10965-280:19(28647-28672)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11042-021-10965-2

Index Terms

A Comprehensive Collaborative Filtering Approach using Autoencoder in Recommender System
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Similarity measures
    2. Retrieval tasks and goals
      1. Information extraction
      2. Recommender systems

Recommendations

Collaborative Variational Autoencoder for Recommender Systems
KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Modern recommender systems usually employ collaborative filtering with rating information to recommend items to users due to its successful performance. However, because of the drawbacks of collaborative-based methods such as sparsity, cold start, etc., ...
CoDFi-DL: a hybrid recommender system combining enhanced collaborative and demographic filtering based on deep learning
Abstract
The cold start problem has always been a major challenge for recommender systems. It arises when the system lacks rating records for new users or items. Addressing the challenge of providing personalized recommendations in the cold start scenario ...
A New Approach for Recommender System
ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and Systems

In today's e-commerce environment, Collaborative Filtering (CF) is a widely used algorithm for recommender system, which is to identify the users who have similar preferences to the target user, and to predict the preference of the target user according ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICCAI '19: Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence

April 2019

267 pages

ISBN:9781450361064

DOI:10.1145/3330482

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 April 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICCAI '19

ICCAI '19: 2019 5th International Conference on Computing and Artificial Intelligence

April 19 - 22, 2019

Bali, Indonesia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
95
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)1

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Tian BLiu SFang J(2023)A Novel Approach with Multi-view Features for Recommendation2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)10.1109/ITAIC58329.2023.10409063(473-477)Online publication date: 8-Dec-2023
https://doi.org/10.1109/ITAIC58329.2023.10409063
Thakker UPatel RShah M(2021)A comprehensive analysis on movie recommendation system employing collaborative filteringMultimedia Tools and Applications10.1007/s11042-021-10965-280:19(28647-28672)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11042-021-10965-2

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten