Abstract
With the proliferation of electronic commerce and knowledge economy environment both organizations and individuals generate and consume a large amount of online information. With the huge availability of product information on website, many times it becomes difficult for a consumer to locate item he wants to buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay, Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of data, changing data, changing user preferences and unpredictable items are faced by these recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce domain which will address issues of cold start problem and change in user preference problem. Our work proposes a novel recommendation system which incorporates user profile parameters obtained from Social Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our model.
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Pareek, J., Jhaveri, M., Kapasi, A., Trivedi, M. (2013). SNetRS: Social Networking in Recommendation System. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_21
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DOI: https://doi.org/10.1007/978-3-642-31552-7_21
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