Clustering and Techniques used in Collaborative Filtering – An Overview
Main Article Content
Abstract
The purpose of this paper is give an overview of the concept of clustering used in recommendation systems, to study the different kind of clustering approaches and techniques getting used in recommendation systems and how implementation of the clusters vary. The paper highlights the pros and cons of using clustering technique in collaborative filtering.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
Pham, M.C., et al. 15.02.2011, “A Clustering Approach for Collaborative Filtering Recommendation Using Social Network Analysis†in Journal of Universal Computer Science, vol 17 no. 4, pp 584 - 604
Das, J., et al., 2014, “Clustering-Based Recommender System Using Principles of Voting Theory†in 2014 International Conference on Contemporary Computing and Informatics, pp 231 - 235
Prado, K., 02.04.2017, How DBSCAN works and why should we use it? , viewed 27th May, 2020 from https://towardsdatascience.com/how-dbscan-works-and-why-should-i-use-it-443b4a191c80.
“Module 7: Voting Theoryâ€, lumen, Mathematics for Liberal arts, viewed 27th May, 2020 from https://courses.lumenlearning.com/waymakermath4libarts/chapter/borda-count/
C. Tran, J. Kim, W. Shin and S. Kim, 02.05.2019, "Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model," in IEEE Access, vol. 7, pp. 62115-62125
Book, T., n.d., “What is Hierarchical Clusteringâ€, viewed 27th May, 2020 from https://www.displayr.com/what-is-hierarchical-clustering/
Lutins, E., 06.09.2017, “DBSCAN: What is it? When to Use it? How to use it.â€, viewed 27th May, 2020 from https://medium.com/@elutins/dbscan-what-is-it-when-to-use-it-how-to-use-it-8bd506293818
Doshi, N, 05.02.2019, “Spectral clustering. The intuition and math behind how it works!â€, viewed 27th May, 2020 from https://towardsdatascience.com/spectral-clustering-82d3cff3d3b7
“Fuzzy c-means clustering algorithmâ€, viewed on 27th May, 2020 from https://sites.google.com/site/dataclusteringalgorithms/fuzzy-c-means-clustering-algorithm
“ A tutorial on Clustering Algorithmsâ€, viewed on 27th May, 2020 from https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/cmeans.html
Wasid M., Ali R., 2018, “An Improved Recommender System based on Multi-criteria Clustering Approach†in 8th International Congress of Information and Communication Technology (ICICT- 2018), pp 93 – 101
Garbade, M.J., 13.09.2018, “Understanding K-means Clustering in Machine Learningâ€, viewed on 27th May, 2020 from Understanding K-means Clustering in Machine Learning https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1
“ A tutorial on Clustering Algorithmsâ€, viewed on 27th May, 2020 from https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
SuperDataScience Team, 29.09.2018, “Self Organizing Maps (SOM's) - K-Means Clustering (Refresher)â€, viewed on 28th May, 2020 from https://www.superdatascience.com/blogs/self-organizing-maps-soms-k-means-clustering-refresher