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also on the rise. But even today, virtually all developers rely on the following
assumption:
If the products (or other content) proposed to a user are those which other users with a
comparable profile in a comparable state have chosen,
then those are the best
recommendations.
Or in other words:
Approach I: What is recommended is statistically what a user would very
probably have chosen in any case, even without recommendations.
This reduces the subject of recommendations to a statistical analysis and
modeling of user behavior. We know from classic cross-selling techniques that
this approach works well in practice.
Yet it merits a more critical examination. In reality, a pure analysis of user
behavior does not cover all angles:
1. The effect of the recommendations is not taken into account: If the user
would probably go to a new product anyway, why should it be recommended at
all? Wouldn't it make more sense to recommend products whose recommenda-
tion is most likely to change user behavior?
2. Recommendations are self-reinforcing: If only the previously “best” recom-
mendations are ever displayed, they can become self-reinforcing, even if
better alternatives may now exist. Shouldn't new recommendations be tried
out as well?
3. User behavior changes: Even if previous user behavior has been perfectly
modeled, the question remains as to what will happen if user behavior suddenly
changes. This is by no means unusual. In web shops, data often changes on a
daily basis: product assortments are changed, heavily discounted special offers
are introduced, etc. Would it not be better if the recommendation engine were to
learn continually and adapt flexibly to the new user behavior?
There are other issues too. The above approach does not take the sequence of all
of the subsequent steps into account:
4. Optimization across all subsequent steps: Rather than only offering the user
what the recommendation engine considers to be the most profitable product in
the next step, would it not be better to choose recommendations with a view to
optimizing sales across the most probable sequence of all subsequent trans-
actions? In other words, even to recommend a less profitable product in some
cases, if that is the starting point for more profitable subsequent products?
To take the long-term rather than the short-term view?
These points all lead us to the following conclusion, which we mentioned right at
the start - while the conventional approach (Approach I) is based solely on the
