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Networked systems are widely applicable in real-world scenarios such associal networks, infrastructure networks, and biological networks. Among thoseapplications, we are interested in social networks due to their complexity andpopularity. One crucial task on the social network is to recommend new contentbased on special characteristics of the graph structure. In this project, weaim to enhance the recommender systems by preventing the recommendations fromleaning towards contents from closed communities. To counteract the bias, wewill consider information dissemination across network as a metric to assessthe recommendation for contents e.g. new connections and news feed. We useacademic collaboration network and user-item interaction datasets from Yelp tosimulate an environment for connection recommendations and to validate theproposed algorithm.

Step Out of Your Comfort Zone: More Inclusive Content Recommendation for Networked Systems