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Survey of Automated Recommender System for Web Applications

Vinutha K.N1 , K.S. Sampada2

Section:Review Paper, Product Type: Conference Paper
Volume-04 , Issue-03 , Page no. 36-39, May-2016

Online published on Jun 07, 2016

Copyright © Vinutha K.N, K.S. Sampada . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Vinutha K.N, K.S. Sampada, “Survey of Automated Recommender System for Web Applications,” International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.36-39, 2016.

MLA Style Citation: Vinutha K.N, K.S. Sampada "Survey of Automated Recommender System for Web Applications." International Journal of Computer Sciences and Engineering 04.03 (2016): 36-39.

APA Style Citation: Vinutha K.N, K.S. Sampada, (2016). Survey of Automated Recommender System for Web Applications. International Journal of Computer Sciences and Engineering, 04(03), 36-39.

BibTex Style Citation:
@article{K.N_2016,
author = {Vinutha K.N, K.S. Sampada},
title = {Survey of Automated Recommender System for Web Applications},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2016},
volume = {04},
Issue = {03},
month = {5},
year = {2016},
issn = {2347-2693},
pages = {36-39},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=58},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=58
TI - Survey of Automated Recommender System for Web Applications
T2 - International Journal of Computer Sciences and Engineering
AU - Vinutha K.N, K.S. Sampada
PY - 2016
DA - 2016/06/07
PB - IJCSE, Indore, INDIA
SP - 36-39
IS - 03
VL - 04
SN - 2347-2693
ER -

           

Abstract

Online shopping new way of business in present days based on the previous surfing and purchasing products are recommended to the users. The existing method of recommending the product has to undergo several processes or functionalities and these processes or functionalities are manually tested for the accuracy. The manual testing method requires lot of time and money and other resources. To overcome the problem this paper proposes a Automation Testing for the recommender system, with Feature Vector Algorithm and perform a automation on each modules of the Feature Vector algorithm and also checks the Cross-Browser compatibility across the browser and also collecting the online reviews from by using Web Crawling Technique.

Key-Words / Index Term

Feature Vector, Recommender System, Cross-Browser Compatibility, Web Crawling Technique

References

[1] Greg Linden, Brent Smith, and Jeremy York Amazon.com Recommendations Item-to-Item Collaborative Filtering • Amazon.com JANUARY • FEBRUARY 2003 Published by the IEEE Computer Society.
[2] Emmanouil Vozalis, Konstantinos G. Margaritis, Analysis of Recommender Systems’ Algorithms, 2001.
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