rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Realtime Data Mining
    sub title: Self-Learning Techniques for Recommendation Engines
  name:
    name part: Paprotny, Alexander.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Thess, Michael.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXIII, 313 p. 100 illus. online resource.
  abstract: 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.
  table of contents: 1 Brave New Realtime World – Introduction -- 2 Strange Recommendations? – On The Weaknesses Of Current Recommendation Engines -- 3 Changing Not Just Analyzing – Control Theory And Reinforcement Learning -- 4 Recommendations As A Game – Reinforcement Learning For Recommendation Engines -- 5 How Engines Learn To Generate Recommendations – Adaptive Learning Algorithms -- 6 Up The Down Staircase – Hierarchical Reinforcement Learning -- 7 Breaking Dimensions – Adaptive Scoring With Sparse Grids -- 8 Decomposition In Transition - Adaptive Matrix Factorization -- 9 Decomposition In Transition Ii - Adaptive Tensor Factorization -- 10 The Big Picture – Towards A Synthesis Of Rl And Adaptive Tensor Factorization -- 11 What Cannot Be Measured Cannot Be Controlled - Gauging Success With A/B Tests -- 12 Building A Recommendation Engine – The Xelopes Library -- 13 Last Words – Conclusion -- References -- Summary Of Notation.
  note: by Alexander Paprotny, Michael Thess.
  subject:
    topic: Mathematics
  subject:
    topic: Computer science
    topic: Mathematics
  subject:
    topic: Computer mathematics
  subject:
    topic: Computer software
  subject:
    topic: Mathematics
  subject:
    topic: Computational Science and Engineering
  subject:
    topic: Mathematical Applications in Computer Science
  subject:
    topic: Mathematical Software
  classification: QA71-90
  classification: 004
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Applied and Numerical Harmonic Analysis
  identifier: 9783319013213
  identifier: http://dx.doi.org/10.1007/978-3-319-01321-3
  record info:
    record creation date: 131203
    record change date: 20151030031016.0
    record identifier: 978-3-319-01321-3