IJSDR
IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: March 2024

Volume 9 | Issue 3

Impact factor: 8.15

Click Here For more Info

Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter

Copyright Infringement Claims
Indexing Partner
Published Paper Details
Paper Title: Prediction of Product Recommendation Using Clustring Technique and Voting Scheme
Authors Name: Versha Patel , Prof. Pritesh Jain
Unique Id: IJSDR1811044
Published In: Volume 3 Issue 11, November-2018
Abstract: Recommender Systems (RS) are generally utilized for giving programmed customized recommendations to data, items and administrations. Community oriented Filtering (CF) is a standout amongst the most well known proposal methods. In any case, with the fast development of the Web regarding clients and things, larger part of the RS utilizing CF strategy experience the ill effects of issues like information sparsity and versatility. In this paper, we present a Recommender System dependent on information bunching methods to manage the versatility issue related with the suggestion errand. We utilize distinctive casting a ballot frameworks as calculations to consolidate suppositions from various clients for prescribing things important to the new client. The proposed work utilize K-MEAN bunching calculation for grouping the clients, and after that execute casting a ballot calculations to prescribe things to the client relying upon the group into which it has a place. The thought is to parcel the clients of the RS utilizing bunching calculation and apply the Recommendation Algorithm independently to each segment. Our framework prescribes thing to a client in a particular group just utilizing the rating insights of alternate clients of that bunch. This encourages us to lessen the running time of the calculation as we keep away from calculations over the whole information. Our goal is to enhance the running time and also keep up a worthy suggestion quality. We have tried the calculation on the Kaggle Product dataset.
Keywords: Recommender Systems, Clustering, Voting System, Scalability.
Cite Article: "Prediction of Product Recommendation Using Clustring Technique and Voting Scheme", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.3, Issue 11, page no.257 - 263, November-2018, Available :http://www.ijsdr.org/papers/IJSDR1811044.pdf
Downloads: 000336256
Publication Details: Published Paper ID: IJSDR1811044
Registration ID:180813
Published In: Volume 3 Issue 11, November-2018
DOI (Digital Object Identifier):
Page No: 257 - 263
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

Click Here to Download This Article

Article Preview

Click here for Article Preview







Major Indexing from www.ijsdr.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

Track Paper
Important Links
Conference Proposal
ISSN
DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to GET DOI and Hard Copy Related
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Creative Commons License
This material is Open Knowledge
This material is Open Data
This material is Open Content
Social Media
IJSDR

Indexing Partner