I am writing my Master thesis, where the goal is to estimate user-item purchase probabilities. In other words, for a given user, what is the probability he/she will buy a certain item. I have session logs (click sessions on items of users) and buys from an e-commerce website.
I found the evaluation to be very hard. Therefore, my approach was the following:
1 Predict the probability that a user will buy something in the next session, given the current session (and earlier sessions). Using this, I can filter out people that have a reasonable probability to buy something at all.
2 I want to take the top 50 items (recommended/ranked by the recommender system currently in place) and from these items, I want to estimate the probability. (As estimating this from all items would be impossible to evaluate at all).
Something else I could do is only looking at the items that a user have clicked on, and estimate those probabilities.
The biggest problem is how to evaluate these probabilities?
Can someone help me define the problem, or give me some hints/tips on how to proceed? If this would not be possible, I have to find another (related) topic to my thesis.