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Title: Recommender System using Social Network Analysis
Authors: Srivasatva, Akash
Jain, Pooja [Guided by]
Keywords: Recommender systems
Collaborative filtering
Facebook query language
Issue Date: 2015
Publisher: Jaypee University of Information Technology, Solan, H.P.
Abstract: Recommender systems are a hot topic in this age of immense data and web marketing. Shopping online is ubiquitous, but online stores, while eminently searchable, lack the same browsing options as the brick-and-mortar variety. Visiting a DVD store in person, a customer can wander over to the science fiction section and casually look around without a particular author or title in mind. Online stores often offer a browsing option, and even allow browsing by genre, but often the number of options available is still overwhelming. Commercial sites try to counteract this overload by showing special deals, new options, and staff favorites, but the best marketing angle would be to recommend items that the user is likely to enjoy or need. Unless online stores want to hire psychics, they need a new technology. The field of data mining has a developing field of research in recommender systems, which fits the bill.
Appears in Collections:B.Tech. Project Reports

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