Scalable Affiliation Recommendation using Auxiliary Networks

TLDR: This article shows that information from the friendship network can indeed be fruitfully exploited in making affiliation recommendations, and suggests two models of user-community affinity for the purpose of making affiliation recommendation: one based on graph proximity, and another using latent factors to model users and communities.

Abstract: Social network analysis has attracted increasing attention in recent years In many social networks, besides friendship links among users, the phenomenon of users associating themselves with groups or communities is common Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups In this article, we tackle the affiliation recommendation problem, where the task is to predict or suggest new affiliations between users and communities, given the current state of the friendship and affiliation networks More generally, affiliations need not be community affiliations---they can be a user’s taste, so affiliation recommendation algorithms have applications beyond community recommendation In this article, we show that information from the friendship network can indeed be fruitfully exploited in making affiliation recommendations Using a simple way of combining these networks, we suggest two models of user-community affinity for the purpose of making affiliation recommendations: one based on graph proximity, and another using latent factors to model users and communities We explore the affiliation recommendation algorithms suggested by these models and evaluate these algorithms on two real-world networks, Orkut and Youtube In doing so, we motivate and propose a way of evaluating recommenders, by measuring how good the top 50 recommendations are for the average user, and demonstrate the importance of choosing the right evaluation strategy The algorithms suggested by the graph proximity model turn out to be the most effective We also introduce scalable versions of these algorithms, and demonstrate their effectiveness This use of link prediction techniques for the purpose of affiliation recommendation is, to our knowledge, novel