Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering
Saniya Zahoor1
- NIT, Srinagar, India.
Section:Research Paper, Product Type: Journal Paper
Volume-6 ,
Issue-4 , Page no. 211-214, Apr-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i4.211214
Online published on Apr 30, 2018
Copyright © Saniya Zahoor . 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: Saniya Zahoor , “Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.211-214, 2018.
MLA Style Citation: Saniya Zahoor "Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering." International Journal of Computer Sciences and Engineering 6.4 (2018): 211-214.
APA Style Citation: Saniya Zahoor , (2018). Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering. International Journal of Computer Sciences and Engineering, 6(4), 211-214.
BibTex Style Citation:
@article{Zahoor_2018,
author = {Saniya Zahoor },
title = {Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2018},
volume = {6},
Issue = {4},
month = {4},
year = {2018},
issn = {2347-2693},
pages = {211-214},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1871},
doi = {https://doi.org/10.26438/ijcse/v6i4.211214}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i4.211214}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1871
TI - Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering
T2 - International Journal of Computer Sciences and Engineering
AU - Saniya Zahoor
PY - 2018
DA - 2018/04/30
PB - IJCSE, Indore, INDIA
SP - 211-214
IS - 4
VL - 6
SN - 2347-2693
ER -
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Abstract
Today Recommender system predicts the future preferences of the user based on the user’s profile. A number of approaches have been taken to address the issue of recommendations, be it user based filtering methods, item-based filtering methods etc. The popular is Collaborative filtering technique used by some renowned companies like Amazon, YouTube and others. But the problem that still holds is the cold start problem and the amount of time and accuracy that is associated with these algorithms. A recent improvement suggested is the Reverse Collaborative filtering for the accuracy and pre-processing time. This paper implements and compares collaborative and reverse collaborative filtering solutions to address the cold start problem.
Key-Words / Index Term
Personalization, Profiles, Recommendation Systems, Cold Start Problem
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