[ynn4k]

Everyday Apple approves around 900 NEW iOS and 50 new Mac Apps in the App Store[1]. In such a huge market, it is quite likely that some would try to game it.

The human curation approach falls flat on such a scale. Machine learning and natural language processing can help us in mining the App Store to detect anomalous behavior and improve the search and discovery of apps.

The statistical models of temporal distributions of ratings and rankings are still emerging and such hightlighting provide a useful resource to train the models. So if you see something, say something.

[1] http://twitter.com/iapps_in

[bigiain]

I wonder if we'll see Google doing a significantly better job in the Android store?

This is an obvious extension to search engine optimization, and I'll bet the guys doing well at it are using many similar techniques to website optimizes.

Goggles web spam team can probably give some great advice to the "app spam team".

[ynn4k]

It is indeed an extension of the "Website optimization for the search engine" or SEO as they call it. To a search engine developer, SEO means to improve the search engine for better output.

The fight against spam is a constantly evolving one and though some of the techniques from "web" search engine could be applied to the App store, there are unique differences between the two - e.g. the rating, download rankings are not directly analogous to page rank. So Google does seem to have some advantage, but the App store problem will need a fresh research approach.