Prediction of Product Recommendation Using Clustring Technique and Voting Scheme
Versha Patel
, Prof. Pritesh Jain
Recommender Systems, Clustering, Voting System, Scalability.
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.
"Prediction of Product Recommendation Using Clustring Technique and Voting Scheme", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.3, Issue 11, page no.257 - 263, November-2018, Available :https://ijsdr.org/papers/IJSDR1811044.pdf
Volume 3
Issue 11,
November-2018
Pages : 257 - 263
Paper Reg. ID: IJSDR_180813
Published Paper Id: IJSDR1811044
Downloads: 000347168
Research Area: Engineering
Country: -, -, -
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
