After looking at the different approaches to filtering for Relevance, I have been seeking a way to map them visually. There are many different startups competing in this space along with the giants, and a way to map them in a matrix would help us see the big picture of how the battle for relevance is evolving on the social web.
What are the fundamental ways in which these approaches and startups differ? These could form the axis around which we can then proceed to map them.
The Popular – Personalized Axis
Filtering either works by showing us the most popular stuff being shared online, or by understanding our individual preferences and surfacing personalized content. Thus, we have the following axis:
The Serendipity – Search Axis
You either search for content or you see it serendipitously without seeking anything specific. Search is actively initiated by the user and is goal-driven, while serendipitous discovery is gifted with the user being passive at the receiving end. This gives us our second axis:
The Filtering for Relevance Matrix (FORMAT)
We combine these two axes to form the backbone of our visualization. We then place different services within our matrix as per their core filtering approach. The result is the Filtering FOR Relevance Matrix (FORMAT) as seen below:
Let us now look at each quadrant closely.
Popular – Search Quadrant
This is the simplest and oldest of all. Search powered by algorithms to surface most popular content online. This also includes other Twitter search services like Topsy. These services are powered by algorithms such as PageRank, PersonRank, Resonance, etc. to surface the most popular result relevant to a query.
This approach dominated the Web 1.0 era before the advent of the social web.
Popular – Serendipity Quadrant
Services in this category help you find the most popular content being shared online across different social networks. These were the next to evolve in the Web 2.0 era, beginning with social bookmarking services like Reddit, StumbleUpon, etc.
There is an element of personalization provided by many of these, in that you “follow” some users, but the motive behind such following is less to seek personalized content, more to seek trending, viral content.
Note how Digg is attempting to move from this quadrant to the personalized quadrant, and facing hurdles along the way.
Search – Personalized Quadrant
A breed of services has evolved around delivering personalized recommendations and content tailored for your needs. Hunch learns about you and acts as a “taste engine”, while Blekko allows you to personalize your searches with slashtags. Google is making forays in this space with its Social Search service, which tries to personalize search results based on your social graph.
Personalized Serendipity Quadrant
This is the hottest space where most of the competition is today.
Twitter Lists are personalized (created by you) and deliver fresh, serendipitous content relevant to your interests. Facebook Likes give you serendipitous discovery from your personal friends. Flipboard provides a social magazine based on your personal social circle on Facebook and Twitter. My6sense delivers new content using ‘Digital Intuition’. Vertical networks like Last.fm deliver music recommendations based on your individual taste. Personalized Twitter newspapers give you fresh content filtered by your social graph on Twitter.
Note how Datasift lies at the center of the matrix. This is because Datasift is a platform providing different filtering services and approaches. Developers may use the platform to develop different services and apps that can lie in any of these quadrants.
How does FORMAT help?
So what is the point of this exercise? Using FORMAT:
- We see the big picture of how services providing relevance and filtering are evolving.
- We see how personalized serendipity is the holy grail of the social web right now.
- We see how different services relate to each other and who is competing with whom and how.
- We see how identifying the target quadrant is important for any new startup in this space.
- We see how users provide friction when a service tries to change quadrants (Digg).
If you are involved in a startup aiming to provide filtered, relevant content to users, which quadrant would you target? See how FORMAT helps?
(c) Mahendra Palsule 2010
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