The battle against information overload is sometimes presented as a choice between Relevance and Popularity, where ‘relevant’ is equated to ‘personalized’ as against popular.
However, Relevance does not always mean Personalized. Relevance is very dynamic – it depends on the needs of a person at a specific point in time. There are times when users want to know about the most popular stories, and other times when they seek personalized content.
There are multiple approaches to filtering information for Relevant Content. Google, Paper.li, and PostRank are examples of algorithmic filtering, while Reddit, Hacker News use a crowdsourcing approach. Klout can be used to filter Twitter streams by influence, while Facebook uses social affinity as a filter for its newsfeed and social signals for its new Comments Plugin. Location is another high-impact signal for delivering relevant content, gaining importance in a mobile world.
In other words, Relevance spans across all the quadrants of the Discovery Matrix above, and none of the above approaches to filtering for relevance is the ‘best approach’. There is no killer approach to Relevance. Henry Nothhaft, Jr., CMO of TrapIt, described it as “the myth of the sweet spot”. The competitive edge will be with services that support multiple discovery methods, multiple filtering approaches, have flexibility, and support multiple mobile platforms.