We have written before about the importance of filter in making sure users find what is out there for them.
One way to limit this content overload and give it some context or ‘trustability’ is through the social graph – what do your actual friends think about it? What would they recommend? Where have they been ? What have they liked?
You can see the value as it replicates real life, but there are clearly some problems and limitations with this approach too.
How many friends have you got? How many friends have you got in the places you want to go to? How many of those friends can you actually trust to give you a good restaurant or hotel review? But finally, and crucially, how many of those friends can you actually access when you need to, to give you those reviews?
A good way to think about this is comparing the power of Search to the power of Bookmarks. Let’s imagine for a minute that Google wasn’t there, and that for any information that you were looking for, you would start browsing from one trusted source, until manually crawling the web you find what you are after. How much more powerful it is to have the wealth of universal data indexed and then ordered by relevance to your query. There is no comparison.
In the same way in a leisure query we need to be able to access and filter vast amounts of data, but in an even more personal way that involves user taste. So what we are talking about is the need for personalised filtered search in the leisure context. Not sharing but filtering. Leveraging all data on the web, powered by all user activity – but made personal by centering it around your own personal activity. That means literally learning from the available data to give the best available answer.
At LikeCube we believe that this is when you really start having something useful.