| Abstract: | The topic of recommender systems gained increasing importance in the nineties, as
the Web became an important medium for business and e-commerce transactions. It was
recognized early on that the Web provided unprecedented opportunities for personalization,
which were not available in other channels. In particular, the Web provided ease in data
collection and a user interface that could be employed to recommend items in a non-intrusive
way.
Recommender systems have grown significantly in terms of public awareness since then.
An evidence of this fact is that many conferences and workshops are exclusively devoted
to this topic. The ACM Conference on Recommender Systems is particularly notable be-
cause it regularly contributes many of the cutting-edge results in this topic. The topic of
recommender systems is very diverse because it enables the ability to use various types
of user-preference and user-requirement data to make recommendations. The most well-
known methods in recommender systems include collaborative filtering methods, content-
based methods, and knowledge-based methods. These three methods form the fundamental
pillars of research in recommender systems. In recent years, specialized methods have been
designed for various data domains and contexts, such as time, location and social infor-
mation. Numerous advancements have been proposed for specialized scenarios, and the
methods have been adapted to various application domains, such as query log mining, news
recommendations, and computational advertising |
| Description: | Although this book is primarily written as a textbook, it is recognized that a large por-
tion of the audience will comprise industrial practitioners and researchers. Therefore, we
have taken pains to write the book in such a way that it is also useful from an applied
and reference point of view. Numerous examples and exercises have been provided to en-
able its use as a textbook. As most courses on recommender systems will teach only the
fundamental topics, the chapters on fundamental topics and algorithms are written with a
particular emphasis on classroom teaching. On the other hand, advanced industrial practi-
tioners might find the chapters on context-sensitive recommendation useful, because many
real-life applications today arise in the domains where a significant amount of contextual
side-information is available. The application portion of Chapter 13 is particularly written
for industrial practitioners, although instructors might find it useful towards the end of a
recommender course |
