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Implementing Social Group Shopping using Support Vector Machines

Author(s): Prasanna Kothalkar | Priyanka Desai

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: icwet;
Issue: 1;
Date: 2012;
Original page

Keywords: Social Shopping | Group Shopping | Artificial Intelligence | Intelligent Systems | Machine Learning | Collective Intelligence | Support Vector Machines | Intelligent Systems

Social media is currently the center of technological innovations and research. The plethora of actionable data made available by modern social networks has brought forth the need for use of intelligent algorithms that can process such volumes of data. Group shopping websites are one of the innovations of such social existence of the web. Many websites such as groupon, livingsocial, dealster, buywithme etc. currently offer some form or the other of group shopping. The paper presents a model of applying SVM algorithm to our case of group shopping with two aims: i) Predicting potential customers for a given product which shall enable us to launch group shopping campaigns more effectively or consider whether they should be launched at all, in the first place. ii) Rather than having open ended campaigns, implementing targeted marketing. An application that implements the above is also presented. If the admin of the website realizes the potential of a product sold by the site or a vendor selling products and services complementary to the social shopping site, he or she may choose to launch the campaign. On doing so, the deal shall be available to all and the potential customers will be specially notified.

Tango Jona
Tangokurs Rapperswil-Jona

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