From Search to Discovery

Source: Techcrunch, Nov 2010

Eric Schmidt’s recent remarks about Google as a “Serendipity Engine” (and Facebook’s quickreply), emphasize an important shift in our daily interaction with the Web and how we use it.  Google-driven search provided us with an expectation of finding what we are looking for with increased precision.  But the rise of Facebook’s social relevance algorithms brought about more personalized content discovery based on the human graph—who we know and what they are reading, watching, or passing along.

In fact, I’d argue that we’re seeing the dominant portion of our interaction with Web content shift from search to discovery.

Jeff Jarvis has perhaps most succinctly defined the concept of serendipity, arguing that serendipity is simply “unexpected relevance.”  His explanation opens an entirely new can of worms, however, in the recognition that relevance is relative.

Editorial Serendipity

Editorial Serendipity is the first and oldest form, the process of combining articles that we know we want to read (the day’s headlines) with unexpected stories (features, profiles, restaurant reviews). Yet the editorial voice and direction of a paper or aggregator is hardly serendipitous; it is a calculation of demographics and readership, whether you’re the New York Times, the Drudge Report, or TechCrunch.

On the plus side here, the human element of editorial serendipity (someone making decisions on what content to deliver) provides an effective flexibility of interest. The downside is that editorial serendipity is delivered by another’s interests, or at best their perception of their audience’s interests. Though the content’s relevance is targeted to a certain demographic of readers, it is a necessarily broad sweep of potential readers, and the level of interest is based on the editors’ perception of what is most in tune with those readers or what she thinks they should be interested in based on her own judgement.

Examples: Newspapers/Magazines, Curated Aggregators

Social Serendipity

Much of our content discovery now comes from the virtual watercooler of what our social circle is sharing directly online. The social aspect of staying informed with what our friends are discussing is valuable, not only for keeping “in the loop,” but also simply for the notion that what our friends like is parallel to our own interests.

The benefit of social serendipity is that our social groups have always been a primary indicator of how we choose to define ourselves and our interests. If something is important or relevant to our friends, there is a high likelihood that it is also relevant to ourselves, as well. The con is that social serendipity is therefore largely public by necessity, and thus a projection of ourselves we would present to others or like to be seen. The propensity to amplify the echo-chamber of like-mindedness is also exaggerated, whereas the goal of serendipity largely lies in the surprise and delight of unexpected content.

Examples: Facebook, Twitter

Crowdsourced Serendipity

Bridging the gap between editorial and social serendipity, the notion of crowdsourced relevance really only delivers a broad, lowest-common-denominator level of content discovery. While not without its usefulness to the degree that we want to be aware of what is most popular and most talked about, the trade-off is the lack of personalization.

The pro here is the viral component, which makes up a great deal of our online content-discovery routines. Crowdsourced serendipity provides a tier of distribution in touch with a larger zeitgeist, from trivial cat videos to important broad-based news. The downside is that the lowest common denominator lacks any precision and therefore has limited utility.

Examples: StumbleUpon, Reddit, Digg

Algorithmic Serendipity

Opposite editorial serendipity, the notion of algorithmic serendipity is the hardest to do well, but the most promising for future innovation. (Bias alert: this is the approach we are trying at TrapIt)

Based-upon any given set of data points, content is personalized to provide both the relevant, need-to-know information of news and content correlating to our interests, with varying degrees of flexibility through both active and passive inputs.

The best aspect of algorithmic serendipity is that it places the user back at the center of defining relevance. Content delivery emanates from the user, whether consciously or in the background based on habit. It also provides for a level of adjustability and fine-tuning based on individualized input and how narrowly or broadly a user may want the information delivered to him.

The con with algorithmic serendipity is that we need to be careful not to completely lose the human element of engagement, no matter how accurate the algorithm is. Of course, the biggest hindrance is that unlike the other forms of serendipity, a finely-tuned algorithmic Serendipity Engine has yet to be effectively realized. Still, it needs to only be the starting point rather than end point of achieving personalized serendipity.

Examples: Genieo, My6Sense, TrapIt

In the end, relevance is a goal based on context.

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