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analysis of historical data, good recommendation engines should model
the
interplay of analysis and action:
Approach II: Recommendations should be based on the interplay of analysis
and action.
In the next chapter, we will look at one such approach of control theory -
reinforcement learning. First though we should return to the question of why the
first approach still dominates current research.
Part of the problem is the limited number of test options and data sets. Adopting
the second approach requires the algorithms to be integrated into realtime applica-
tions. This is because the effectiveness of recommendation algorithms cannot be
fully analyzed on the basis of historical data, because the effect of the recommen-
dations is largely unknown. In addition, even in public data sets, the recommenda-
tions that were actually made are not recorded (assuming recommendations were
made at all). And even if recommendations had been recorded, they would mostly
be the same for existing products because the recommendations would have been
generated manually or using algorithms based on the first approach.
This trend was further reinforced by the Netflix competition [Net06]. The
company Netflix offered a prize of 1 million dollars to any research team which
could increase the prediction accuracy of the Netflix algorithm by 10 % using a
given set of film ratings. The Netflix competition was undoubtedly a milestone in
the development of recommendation systems, and its importance as a benchmark
cannot be overstated. But it pushed the development of recommendation algorithms
firmly in the direction of pure analytics methods based on the first approach.
So we can see that on practical grounds alone, the development of viable
recommendation algorithms is very difficult for most researchers. However, the
number of publications in the professional literature treating recommendations as a
control problem and adopting the second approach has been on the increase for
some time.
As a further boost to this way of thinking, prudsys AG chose the theme of
recommendation algorithms for its 2011 Data Mining Cup, one of the world's
largest data mining competitions [DMC11]. The first task related to the classical
problem of pure analysis, based however on transaction data for a web shop. But the
second task looked at realtime analytics, asking participants to design a recommen-
dation program capable of learning and acting in realtime via a defined interface.
The fact that over 100 teams from 25 countries took part in the competition shows
the level of interest in this area.
A further example of new realtime thinking is the RECLAB project of
RichRelevance, another vendor of recommendation engines. Under the slogan
“If you can't bring the data to the code, bring the code to the data,” it offers
researchers to submit their recommendation code to the lab. There, new algorithms
can be tested in personalization applications on live retail sites.
