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Jay Anthony
7 January 2013 | 8 min read

PopCore: A system for Network-Centric Recommendations is a research thesis by Meethu Malu, Sr. UX Designer at TECHVED Consulting. Abstract: In this paper we explore the idea of network-centric recommendations. In contrast to individually-oriented recommendations enabled by network data, a network-centric approach to recommendations introduces new goals such as effective information exchange, enabling shared experiences, and supporting user-initiated suggestions in addition to conventional goals like recommendation accuracy. We are building a Facebook application, PopCore, to study how to support these goals in a real network, using recommendations in the entertainment domain. We describe the design and implementation of the system and initial experiments. We end with a discussion on a set of possible research questions and short-term goals for the system. Keywords recommender systems, social recommendation, network-centric
1. INTRODUCTION
Users are increasingly disclosing information about themselves and their relationships on social websites such as Facebook, Twitter, and Google+. These data provide signals that have been used to augment traditional collaborative altering techniques by making network-aware recommendations [8, 9]. Such recommenders use social data to support prediction, provide social context for the recommendations, and help alleviate the cold-start problem typically found in recommender systems. Much of their power comes from social forces, such as homophily, trust and in uence, and thus these recommenders do not just provide better recommendations, they can also support the study of these forces.
For example, in [4], the authors divide a user’s social contacts into familiarity and similarity networks (proxies for trust and homophily, respectively), and study their relative impact on the quality of recommendation. But we can take this a step farther.
Just as a user’s network can influence the recommendations he/she receives, Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro?t or commercial advantage and that copies bear this notice and the full citation on the ?rst page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speci?c permission and/or a fee.

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