Recommending Contacts in Social Networks Using Information Retrieval Models

The fast expansion of online social networks has given rise to new challenges and opportunities for information retrieval and, as a particular area, recommender systems. A particularly compelling problem in this context is recommending contacts, that is, automatically predicting people that a given user may wish or benefit from connecting to in the network. This task has interesting particularities compared to more traditional recommendation domains, a salient one being that recommended items belong to the same space as the users they are recommended to. In this paper, we explore the connection between the contact recommendation and the information retrieval (IR) tasks. Specifically, we research the adaptation of IR models for recommending contacts in social networks. We report experiments over data downloaded from Twitter where we observe that IR models are competitive compared to state-of-the art contact recommendation methods.