Database Reference
In-Depth Information
Chapter 2
Strange Recommendations?
On the Weaknesses of Current
Recommendation Engines
Abstract Currently, most approaches to recommendation engines focus on
traditional techniques such as collaborative filtering, basket analysis, and content-
based recommendations. Recommendations are considered from a prediction point
of view only, i.e., the recommendation task is reduced to the prediction of content
that the user is going to select with highest probability anyway. In contrast, in this
chapter we propose to view recommendations as control-theoretic problem by
investigating the interaction of analysis and action. The corresponding mathemat-
ical framework is developed in the next chapters of the topic.
2.1
Introduction to Recommendation Engines
Recommendation engines (REs) for customized recommendations have become
indispensable components of modern web shops. REs offer the users additional
content so as to better satisfy their demands and provide additional buying appeals.
There are different kinds of recommendations that can be placed in different
areas of the web shop. “Classical” recommendations typically appear on product
pages. Visiting an instance of the latter, one is offered additional products that are
suited to the current one, mostly appearing below captions like “Customers who
bought this item also bought” or “You might also like.” Since it mainly respects the
currently viewed product, we shall refer to this kind of recommendation, made
popular by Amazon, as product recommendation. Other types of recommendations
are those that are adapted to the user's buying behavior and are presented in a
separate area as, e.g., “My Shop,” or on the start page after the user has been
recognized. These provide the user with general but personalized suggestions
with respect to the shop's product range. Hence, we call them personalized
recommendations.
Further recommendations may, e.g., appear on category pages (best
recommendations for the category), be displayed for search queries (search recom-
mendations), and so on. Not only products but also categories, banners, catalogs,
