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1 Introduction to Recommender Systems Handbook 17
relevant measures are suggested. Among the list of properties are: coverage, cold
start, confidence, trust, novelty, risk, and serendipity.
When discussing the practical aspects of RSs, it may be beneficial to analyze
real system implementations. The idea is to test theoretically intuitive assumptions
in order to determine if they work in practice. The major problem that one must face
in this case comes from the fact that the owners of commercial RSs are generally
unwilling to reveal their practices and there are only relatively few opportunities for
such cooperation.
Chapter 9 reports on such an opportunity and describes the operation of a real
RS, illustrating the practical aspects that apply to the implementation stage of the
RS development and its evaluation. This description focuses on the integration of
a RS into the production environment of Fastweb, one of the largest European IP
Television (IPTV) providers. The chapter describes the requirements and consider-
ations, including scaling and accuracy, that led to the choice of the recommender
algorithms. It also describes the off-line and on-line evaluations that took place and
illustrates how the system is adjusted accordingly.
1.6 Recommender Systems and Human Computer Interaction
As we have illustrated in previous sections, researchers have chiefly been concerned
with designing a range of technical solutions, leveraging various sources of knowl-
edge to achieve better predictions about what is liked and how much by the target
user. The underlying assumption behind this research activity is that just presenting
these correct recommendations, i.e., the best options, should be enough. In other
words, the recommendations should speak for themselves, and the user should def-
initely accept the recommendations if they are correct. This is clearly an overly
simplified account of the recommendation problem and it is not so easy to deliver
recommendations.
In practice, users need recommendations because they do not have enough
knowledge to make an autonomous decision. Consequently, it may not be easy for
them to evaluate the proposed recommendation. Hence, various researchers have
tried to understand the factors that lead to the acceptance of a recommendation by a
given user [105, 30, 24, 97, 33].
[105] was among the first to point out that the effectiveness of a RS is depen-
dent on factors that go beyond the quality of the prediction algorithm. In fact, the
recommender must also convince users to try (or read, buy, listen, watch) the rec-
ommended items. This, of course, depends on the individual characteristics of the
selected items and therefore on the recommendation algorithm. The process also
depends, however, on the particular human/computer interaction supported by the
system when the items are presented, compared, and explained. [105] found that
from a users perspective, an effective recommender system must inspire trust in
the system; it must have a system logic that is at least somewhat transparent; it
should point users towards new, not-yet-experienced items; it should provide details