I'm trying to build a recommendation engine for an e-commerce site. By using the common recommendation approach, I'm assuming that each product I recommend has the same value, so all I need to do is optimize the conversion rate probably using a common recommendation algorithm, but when the product's price varies a lot, what I really need to optimize is the following formula for each user:
Value of recomendation = (probability to convert) * (product price)
The bigger problem than choosing the right algorithm and approach is choosing the right metric, so I could compare the different algorithms. For example, if you would like to only optimize the conversion rate, I would use the precision and recall or false/positive metrics.
What metrics and approaches/algorithms are recommended in this case?