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Describing novel mathematical concepts for recommendation engines,
Realtime Data Mining: Self-Learning Techniques for Recommendation
Engines features a sound mathematical framework unifying approaches
based on control and learning theories, tensor factorization, and
hierarchical methods. Furthermore, it presents promising results of
numerous experiments on real-world data. The area of realtime data
mining is currently developing at an exceptionally dynamic pace,
and realtime data mining systems are the counterpart of today's
"classic" data mining systems. Whereas the latter learn from
historical data and then use it to deduce necessary actions,
realtime analytics systems learn and act continuously and
autonomously. In the vanguard of these new analytics systems are
recommendation engines. They are principally found on the Internet,
where all information is available in realtime and an immediate
feedback is guaranteed. This monograph appeals to computer
scientists and specialists in machine learning, especially from the
area of recommender systems, because it conveys a new way of
realtime thinking by considering recommendation tasks as
control-theoretic problems. Realtime Data Mining: Self-Learning
Techniques for Recommendation Engines will also interest
application-oriented mathematicians because it consistently
combines some of the most promising mathematical areas, namely
control theory, multilevel approximation, and tensor factorization.
Describing novel mathematical concepts for recommendation engines,
Realtime Data Mining: Self-Learning Techniques for Recommendation
Engines features a sound mathematical framework unifying approaches
based on control and learning theories, tensor factorization, and
hierarchical methods. Furthermore, it presents promising results of
numerous experiments on real-world data. The area of realtime data
mining is currently developing at an exceptionally dynamic pace,
and realtime data mining systems are the counterpart of today's
"classic" data mining systems. Whereas the latter learn from
historical data and then use it to deduce necessary actions,
realtime analytics systems learn and act continuously and
autonomously. In the vanguard of these new analytics systems are
recommendation engines. They are principally found on the Internet,
where all information is available in realtime and an immediate
feedback is guaranteed. This monograph appeals to computer
scientists and specialists in machine learning, especially from the
area of recommender systems, because it conveys a new way of
realtime thinking by considering recommendation tasks as
control-theoretic problems. Realtime Data Mining: Self-Learning
Techniques for Recommendation Engines will also interest
application-oriented mathematicians because it consistently
combines some of the most promising mathematical areas, namely
control theory, multilevel approximation, and tensor factorization.
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