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Personalized Recommender System using Collaborative Filtering Technique and Pyramid Maintenance Algorithm

by Minakshi Pachpatil, Anjana N. Ghule
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 8
Year of Publication: 2016
Authors: Minakshi Pachpatil, Anjana N. Ghule
10.5120/ijca2016908526

Minakshi Pachpatil, Anjana N. Ghule . Personalized Recommender System using Collaborative Filtering Technique and Pyramid Maintenance Algorithm. International Journal of Computer Applications. 136, 8 ( February 2016), 25-31. DOI=10.5120/ijca2016908526

@article{ 10.5120/ijca2016908526,
author = { Minakshi Pachpatil, Anjana N. Ghule },
title = { Personalized Recommender System using Collaborative Filtering Technique and Pyramid Maintenance Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 8 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 25-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number8/24175-2016908526/ },
doi = { 10.5120/ijca2016908526 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:36:33.909656+05:30
%A Minakshi Pachpatil
%A Anjana N. Ghule
%T Personalized Recommender System using Collaborative Filtering Technique and Pyramid Maintenance Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 8
%P 25-31
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Location aware recommender system(LAS) make use of spatial ratings for generating personalized recommendations.It uses Collaborative filtering techniques to generate recommendations based on user location,item location or both user and item location . LAS uses spatial ratings for spatial items , Spatial ratings for Non-spatial items, Non spatial ratings for spatial items to generate personalized recommendation. For spatial ratings for non spatial items LAS uses user partitioning technique where spatial ratings are distributed as per user location in the pyramid. Pyramid Maintenance algorithm provided to achieve required scalability or locality.LAS is scalable as number of γ-cells are increased in pyramid and to improve locality α-cells are increased to maintain CF Model. LAS is efficient as compare to traditional recommendation system because algorithm provided is strong enough to cope challenge of locality and scalability.

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Index Terms

Computer Science
Information Sciences

Keywords

Recommender system spatial location locality pyramid structure.

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