Graduation Year

2009

Document Type

Thesis

Degree

M.S.

Degree Granting Department

Information Systems

Major Professor

Balaji Padmanabhan, Ph.D.

Keywords

Social pricing, Recommender systems, Social networks, E-commerce, Trust

Abstract

Social shopping is one of the latest trends on the Internet. Websites dedicated to social networking with a focus on shopping have been emerging on the web for a few years. The basic idea is that consumers are looking for product information on the Internet and social shopping sites provide a place for consumers to find this information from other consumers. These sites provide a place for their users to engage in socialization and shopping simultaneously, sometimes following recommendations of premier users, who are labeled from other users. However, purchases aren't made through these sites. So, there may still be something missing from the experience. For these sites, social pricing mechanisms may be implemented to provide revenue. Major ecommerce websites have begun focusing on increasing social features throughout the transaction process. For example, more websites are including ratings, reviews and recommendations of products and services by other consumers.

However, pure ecommerce websites do not provide functionality that allows consumers to communicate in real time. Hence, there are some features missing from the social experience. Also, the social functionality included in pure e-commerce websites, tends to be utilized for the benefit of the Web site, as opposed to the consumers. Both social shopping sites and ecommerce sites have seen independently successful though few sites have been able to truly integrate these together at this point. It may be more beneficial to the end user if these sites could work in unison. This thesis is an exploratory study of the emerging social shopping phenomenon.

The contributions of this work include analysis of the social shopping phenomenon and identifying metrics and Web sites that incorporate social shopping, a survey of academic literature related to social shopping and social pricing and a review of current recommender system algorithms with a discussion on how to incorporate social networking data into the algorithms to improve recommendations. Improvement suggestions include incorporating customer purchase history with social networking information. Potential future research ideas are included.

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