A Recommender System would measure the correlation between ratings of different users and yield recommendations for a given user about the items which may be of interest to him.
However, tastes change over time so old ratings might not reflect current preferences and vice versa. You may once have put "excellent" to a book you would now rate as "not too disgusting" and so on. Moreover, the interests themselves do change as well.
How should recommender systems work in a changing environment?
- One option is to cut off the "old" ratings, which may work just fine assuming you correctly define "old" (you can even say ratings never expire and pretend that the problem doesn't exist). But it's not the best possible option: of course tastes evolve, it's a normal life flow, and there's no reason why we cannot use the extra knowledge of once correct past ratings.
- Another option is to somehow accommodate this extra knowledge. Thus we could not just find an "instant match" for your current interests but suggest you the things you may like next (as opposed to the things you may like now).
I'm not sure if I'm explaining this well enough. Basically I'm in favor of the second approach and am talking about a Recommender System which would measure the correlations of taste trajectories and yield recommendations which will cater for.. well, let's call it personal growth - because they will be coming from people whose "tastes trajectory" (and not just "tastes snapshot") is similar to yours.
Now the question: I wonder if something similar to the "option 2" already exists and, if it does, I wonder how it works. And if it doesn't exist, you're welcome to discuss how it should work! :)