I work with recommender systems on daily basis and have also never heard of the application of such a model as recommender system. I can only speculate about the reasons though.
The main overall reason might be that recommender systems are often applied in a domain where the price/cost of an item is too small to force the customer
to invest time into making a nearly-optimal decision, maximizing his utility. This should be kept in mind in the following section. Such domains
include e-commerce or news portals (where articles are recommended) or sites like tastekid.com, where the decision at this step costs only a click, i.e. virtually nothing.
The described conditional multinomial model requires (or works best) with ...
- characteristics of the customer
- characteristics of the items
- assumed rationality when it comes to the decision
Let's step through every point
Characteristics of the customers
Beside some basic demographic information like gender, address and (may be) age, little is known. The less the price of an item (see above),
the harder it is to request a survey before the selection process starts. Activity data (bought items, ratings etc.) on the other hand can be collected
without any work from the customer and can be used to describe the customer, following the motto "you are what you are interested in". The items
the customer is interested in (the preferences) implicitly capture what is important to the customer.
Characteristics of the items
Building a model based on the characteristics of a item is already done, either via "content based collaborative filtering" or a model based approach.
These are e.g. used to solve the cold-start-problem, i.e. the fresh new recommender system has not (enough) preferences yet.
The drawback here however is that is hard to automatically collect the properties of an item. Imagine the case of fashion: Some are easy (color, brand),
some are very hard (how does the cloth feels like on skin, how does it look if my hip is broader than average). Sometime it is completely impossible because
it entirely depends on the reception of the product, e.g. in case of movies. For certain items, such information can be collected by humans or by a very very sophisticated
system understanding semantics and language. It is not clear that the resulting improvement will outweigh the costs.
So instead of saying: "Item A is similar to item B due to the properties p1,p2,...," it is easier to say "a lot of people have both bought item A and item B.
I don't know why, but they are similar enough for the purpose of a recommender system". So the preferences implicitly capture how similar to items are.
Assumed rationality when it comes to the decision
We are humans and we pretend to be rational all the time. If e.g. the price or other circumstances forces us to think hard about it a decision, it might be the
case that the rational part of a decision is higher than average. But when it comes to utilize advertising to sell people stuff (and yes, recommendations can be
seen as advertising), marketing will tell that rationality plays a lesser role.
Additionally, people are often do not know beforehand which properties are most important to them in order to maximize their personal utility function.
If this would be the case, all buying processes could be described via the usage of a search engine, where
a) all relevant properties are listed
b) the customer selects all properties relevant to him and name the product of interest
and the search engine delivers exactly the right results.
Instead, people have a basic goal (e.g. buying a suit), but then are browsing around to see how products appeal to them and / or to get inspired.
Making a buy decision is still partially rational (budget, invested time) but often comes down to "what feels right". Of course, every domain has its
own distribution of rationality and emotionality. The more technical, the more facts do play an important role. But
even than the customer might select a brand due to the curtain fire of advertising, which he would not have named as primary criterion beforehand.
So building a economic model here might be still be working and it surely correct, buth might be entirely over the top. Additionally, one might have
to build a separate model for each type of item a shop is selling.
Building a recommender system entirely based on preferences is often done because ...
- it is simple (=> cheap)
- it can be done automatically, no extra work from the customer is required (=> cheap)
- it works (good enough), so that a more complicated model might not outweigh the additional costs.
But: There are domains, where such a economic model will be better. I do not doubt, that a good estate agent and hence a good expert system based
on a economic model will easily outperform a recommender system based on preferences. I have regularly observed that recommendations made by human experts are often
better than automatic ones. However, the automatics are still good and can produced en mass without too much costs, so that an expert can focus
on more sophisticated tasks.