Hi, I'm planning to use the concept of collaborative filtering to recommend user interface elements to users. The users implicitly rate the user interfaces by choosing them. The rating scale probably will be binary (1 = chosen, 0 = rejected/unchosen).
I'm considering a user-based approach, because I can use some additional data describing the user, which should be relevant to the choice of user interface elements. So the user similarity can be calculated as a combination of their ratings and the additional data. There are 30 user interface elements, which can be chosen independently of each other. In the literature I read, there are always a lot more items that are rated. (e.g. two million books) Are there any problems to use collaborative filtering with only 30 items? Are there too little items to calculate an accurate correlation between users? Could the additional data about the users be weighted differently than the ratings of the user interface elements? Thank you Anthony
