Approach for mapping consumer preferences I have this web application where I need to map consumer preferences based on some input information and individual choices. My goal is to create a list of product recommendations and evaluate the level of “importance” of these products with respect to the user.  
From my research so far I realize that there are several ways to address this problem. For instance there is the classical marketing research approach that involves modeling individual utility functions and econometric models. Alternatively,  there’s  the machine learning approach with learning based algorithms. There might be others I’m not aware of. 
Which would be the best approach in this case?  Are there other alternatives? I really could use some direction on the best way to go.
 A: it depends on the data you have: do you have purchases identified by buyer, corresponding prices, other information about the buyer? information about the products?
Most of today's econometric consumer choice models (aids, quaids, easi) use a form close to Holbrook Working's form, something like: 
$w_n^i = \alpha^i + \beta^i \log(y_n) + \sum_{k=1}^J \gamma^i_k \log(price_k) + \sum_{l=1}^L \delta^i_l socio^l_n + \epsilon_n $
where $w^i_n$ is the share of the expenditure of consumer $n$ on good $i$ in her total expenditure, $y_n$ is the total expenditure of consumer $n$, $socio^l_n$ is the $l$th of $L$ socio-demographic control variables for consumer $n$, and $\epsilon$ is the error term.
This form fits remarkably well on many macro and micro consumer expenditure datasets, so it is worth a try. It is straightforward to fit (if you don't need to conform to microeconomic theory of consumer choice), and easily gives you the estimated effect of a change in total expenditure or price on the quantities purchased. That would be the economic way of evaluating the importance of products. However usually it is used on a small number of broad categories of goods, and not on hundreds of products as in a webshop.
Alternatively, you can use machine learning methods for regression (predict the probability of buying a given object?) or unsupervized learning (to see which kinds of consumers are "close" to given products). And I think there is also a whole literature about recommender systems, but I don't know anything about that.
