Compressive Review On Mining Competitors From Large Unstructured Datasets

Authors(2) :-K. Hari Krishna, Badarla Sravani

In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the Competitiveness between two items? Who are the main competitors of a given item? What are the features of an Item that most affect its competitiveness? Despite the impact and relevance of this problem too many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal Definition of the competitiveness between two items based on the market segments that they can both cover. Our Evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a Wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the Quality of our results and the scalability of our approach using multiple datasets from different domains.

Authors and Affiliations

K. Hari Krishna
MCA Department, Vignan's Lara Institute of Technology and Science, Vadlamudi, Guntur, Andhra Pradesh, India
Badarla Sravani
MCA Department, Vignan's Lara Institute of Technology and Science, Vadlamudi, Guntur, Andhra Pradesh, India

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Publication Details

Published in : Volume 4 | Issue 2 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 01-07
Manuscript Number : CSEIT1833606
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

K. Hari Krishna, Badarla Sravani, "Compressive Review On Mining Competitors From Large Unstructured Datasets", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 2, pp.01-07, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833606

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