Recommender systems, software programs that learn from human
behavior and make predictions of what products we are expected to
appreciate and purchase, have become an integral part of our
everyday life. They proliferate across electronic commerce around
the globe and exist for virtually all sorts of consumable goods,
such as books, movies, music, or clothes.
At the same time, a new evolution on the Web has started to take
shape, commonly known as the "Web 2.0" or the "Social Web"
Consumer-generated media has become rife, social networks have
emerged and are pulling significant shares of Web traffic. In line
with these developments, novel information and knowledge artifacts
have become readily available on the Web, created by the collective
effort of millions of people.
This textbook presents approaches to exploit the new Social Web
fountain of knowledge, zeroing in first and foremost on two of
those information artifacts, namely classification taxonomies and
trust networks. These two are used to improve the performance of
product-focused recommender systems: While classification
taxonomies are appropriate means to fight the sparsity problem
prevalent in many productive recommender systems, interpersonal
trust ties - when used as proxies for interest similarity - are
able to mitigate the recommenders' scalability problem.
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