Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages) that are likely of interest to the user. Typically, a recommender system compares the user's profile to some reference characteristics, and seeks to predict the 'rating' that a user would give to an item they had not yet considered. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach).

When building the user's profile a distinction is made between explicit and implicit forms of data collection.

So, here at Osmosoft been lucky enough to have access to an iPhone to play with for the last few weeks. Our first discovery was that TiddlyWiki works just fine on the iPhone - apart from being excruciatingly slow. TiddlyWiki is so meaty you really feel the 20x speed difference between a MacBook Pro and an iPhone. Anyhow, I’m confident that we can slim TiddlyWiki down to the extent that it runs at a reasonable speed on these new portable slow AJAX devices. (If you try the leaner and meaner first version of TiddlyWiki on an iPhone, it’s a good deal snappier).

The other problem is that saving doesn’t work properly because the TiddlyWiki is being accessed via http. We can expect that TiddlyWiki adaptations that support serverside saving (like TiddlySpot) will work, but that doesn’t re ...