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Enhancing Prediction in Collaborative Filtering-Based Recommender Systems

M. Hatami1 , S. Pashazadeh2

Section:Research Paper, Product Type: Journal Paper
Volume-2 , Issue-1 , Page no. 48-51, Jan-2014

Online published on Feb 04, 2014

Copyright © M. Hatami, S. Pashazadeh . 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: M. Hatami, S. Pashazadeh, “Enhancing Prediction in Collaborative Filtering-Based Recommender Systems,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.48-51, 2014.

MLA Style Citation: M. Hatami, S. Pashazadeh "Enhancing Prediction in Collaborative Filtering-Based Recommender Systems." International Journal of Computer Sciences and Engineering 2.1 (2014): 48-51.

APA Style Citation: M. Hatami, S. Pashazadeh, (2014). Enhancing Prediction in Collaborative Filtering-Based Recommender Systems. International Journal of Computer Sciences and Engineering, 2(1), 48-51.

BibTex Style Citation:
@article{Hatami_2014,
author = {M. Hatami, S. Pashazadeh},
title = {Enhancing Prediction in Collaborative Filtering-Based Recommender Systems},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2014},
volume = {2},
Issue = {1},
month = {1},
year = {2014},
issn = {2347-2693},
pages = {48-51},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=39},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=39
TI - Enhancing Prediction in Collaborative Filtering-Based Recommender Systems
T2 - International Journal of Computer Sciences and Engineering
AU - M. Hatami, S. Pashazadeh
PY - 2014
DA - 2014/02/04
PB - IJCSE, Indore, INDIA
SP - 48-51
IS - 1
VL - 2
SN - 2347-2693
ER -

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Abstract

Recommender systems (RS) are introduced to help users with finding the desired information. Collaborative filtering (CF) approach is one of the most widely used techniques in recommender systems. Prediction is the main part of all recommender systems. In this paper we propose an enhanced prediction formula which could be employed in all CF-based methods. We used Resnick prediction formula as a base because it�s the most well-known and employed formula in CF-based RS. In the formula we have used not only the average of active user�s ratings, but also the collective average of similar users� ratings and the average of all ratings given to the target item. The results are promising and satisfying. We compared the results of enhanced prediction formula to the unenhanced version to verify the effectiveness of our proposed method.

Key-Words / Index Term

Collaborative Filtering, Recommender Systems, Prediction Formula, Enhancement

References

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