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authors (in book shops), etc., may be recommended. Even more, as an ultimate goal,
recommendation engineering aims at a total personalization of the online shop,
which includes personalized navigation, advertisements, prices, mails, and text
messages. The amount of prospects is seemingly inexhaustible. For the sake of
simplicity, however, this topic will be restricted to mere product recommendations -
we shall see how complex even this task is.
Recommendation engineering is a vivid field of ongoing research. Hundreds
of researchers, predominantly from the USA, are tirelessly devising new theories
and methods for the development of improved recommendation algorithms.
Why, after all?
Of course, generating intuitively sensible recommendations is not much of a
challenge. To this end, it suffices to recommend top sellers of the category of the
currently viewed product. The main goal of a recommendation engine, however,
is an increase of the web shop's revenue (or profit, sales numbers, etc.). Thus, the
actual challenge consists in recommending products that the user actually visits and
buys, while, at the same time, preventing down-selling effects, so that the recom-
mendations do not simply stimulate buying substitute products and, therefore, in the
worst case, even lower the shop's revenue.
This brief outline already gives a glimpse at the complexity of the task. It is even
worse: many web shops, especially those of mail-order companies (let alone book-
shops), by now have hundreds of thousands, even millions, of different products on
offer. From this giant amount, we then need to pick the right ones to recommend!
Furthermore, through frequent special offers, changes of the assortment as well as -
especially in the area of fashion - prices are becoming more and more frequent.
This gives rise to the situation that good recommendations become outdated soon
after they have been learned. A good recommendation engine should hence be in a
position to learn in a highly dynamic fashion. We have thus reached the main topic
of the topic - adaptive behavior.
We abstain from providing a comprehensive exposition of the various
approaches to and types of methods for recommendation engines here and refer
to the corresponding literature, e.g., [BS10, JZFF10, RRSK11]. Instead, we shall
focus on the crucial weakness of almost all hitherto existing approaches, namely,
the lack of a control theoretical foundation, and devise a way to surmount it.
2.2 Weaknesses of Current Recommendation Engines
and How to Overcome Them
Recommendation engines are often still wrongly seen as belonging to the area of
classical data mining. In particular, lacking recommendation engines of their own,
many data mining providers suggest the use of basket analysis or clustering
techniques to generate recommendations. Recommendation engines are currently
one of the most popular research fields, and the number of new approaches is
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