Homophily—the principle that “birds of a feather flock together”— have been studied extensively in online network formation.
Connectivity breeds similarity and targeting the social network neighbors is an efficient strategy of audience identification for advertisers or for matching up people that have similar interests.
Services that use Facebook Open Graph to push out activities of their users herald this as great way to discover new and interesting objects such as movies, restaurants, and products. Netflix recent integration with Facebook is a good example.
The Facebook’s Graph Search introduction showed a similar use case: restaurant recommendations by friends. Besides the question of density of such “likes”, I question the value of recommendation services based on social activities of ones family, closest friends or colleges.
Assuming that these are the people one communicates the most with and therefore one is most similar with recommendation most likely lack diversity. Like in midlevel settlings in the countryside where people could not travel further than ~5 miles life would become very boring very quickly.
Heterophily – “the love of the different” – describes human tendency to organize in diverse groups. Network formation in this case organizes homophilious networks through weak ties into a larger heterophilious network.
Heterophilious network allow the spreading of new and unique content creating serendipity. This correlates with finding experts or taste masters in the offline world; recommendations by subject matter experts.
Algorithms that use social activity to recommend unique or tail content have to take this into account. They would have to aggregate my activities (e.g. likes) and that of my network and find similar clusters of users in the open graph. These clusters could be used to derive new and interesting recommendations analogous to collaborative filtering algorithms.
Until such algorithms come into existence I will stick with strangers on Yelp or Zagat and the friendly concierge.