An Improved Online Book Recommender System using Collaborative Filtering Algorithm

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
E. Uko Okon, B. O. Eke, P. O. Asagba
10.5120/ijca2018917193

Uko E Okon, B O Eke and P O Asagba. An Improved Online Book Recommender System using Collaborative Filtering Algorithm. International Journal of Computer Applications 179(46):41-48, June 2018. BibTeX

@article{10.5120/ijca2018917193,
	author = {E. Uko Okon and B. O. Eke and P. O. Asagba},
	title = {An Improved Online Book Recommender System using Collaborative Filtering Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2018},
	volume = {179},
	number = {46},
	month = {Jun},
	year = {2018},
	issn = {0975-8887},
	pages = {41-48},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume179/number46/29486-2018917193},
	doi = {10.5120/ijca2018917193},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In e-commerce today, contents available for users to explore are overwhelming because an average ecommerce website is about seventy per cent (70%) more than a physical store in total number of users and items. Hence, the need to filter, prioritize and efficiently deliver relevant information using recommender systems. We will design and develop a recommendation model that uses object-oriented analysis and design methodology (OOADM), improved collaborative filtering algorithm and an efficient quick sort algorithm to solve these problems. This will be achieved by implementing the stated model with python model-view-controller (MVC) framework known as Django Framework. This improved system is implemented using a real-time, cloud-hosted NOSQL database called FireBase which guarantees scalability. From the results, the speed and scalability of book recommendation was improved with a performance record obtained within the range of ninety (90) to ninety-five (95) per cent using the root mean square error (RMSE) of several recommendations obtained from the system.

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Keywords

Recommender system, collaborative filtering, recommender algorithms, collaborative filtering algorithm, machine learning algorithm, NoSQL, Firebase, scalability.