In the light of other link aggregators like Digg, Stumbleupon and Delicious; and more recently, Twine try to make the gems of the web more findable. And of course, findability is part of Peter Morville’s famous UE honeycomb so this is obviously important to people, and this can be applied by saying that we must be able to find things easily on the web. For locating that one website that answers a simple question, searching is immensely useful, but on a research project, branching out to content that may offer you lateral answers can be more difficult without asking others. A friend mentioned a research project where the web is scoured for a solution, but when you run out of hyperlinks to follow and search keywords to try, social website recommenders can offer help in finding that evasive little gem.
Everyone is incorporating social into their web applications, to the point where there is so much noise created by the new startups. The few that do emerge as being widely used can provide inspiration for the mobile. We can take aspects from the successes, remix them, and apply them to other settings. Notable here, as part of my ongoing interests in mobile UE, is the importance of mobile search. When we’re out and about we may want to find out that quick snippet of information nagging you on a train view. Or maybe you want to find out the best restaurant in an unfamiliar area. These are typical use scenarios that are immediately obvious. If we turned the latter example on its head, and describe a use scenario where people want to find better restaurants in a familiar environment, the situation becomes more difficult. No longer would users be content with a simple average “star rating” given by other visitors to the restaurant. How about finding the best steak amongst the local chinese take-aways? (Believe me, people love these wok-fried steaks.) How about the best place for a quick desert, that offers a comfortable local atmosphere?
Whoever provides a solution that lets you quickly assess the surrounding areas for recommendations based on relevant filters based on social context, specific culinary preferences, time of day, and dynamically changing qualities of restaurants that in the past can only come from word of mouth are sure to make a few bucks. Maybe we can take inspiration from (social) web 2.0 and bring this into (social) mobile 2.0.