Wednesday, December 27, 2017

Sort of Recommender Systems

  1. Collaborative filtering methods 
    1. Memory-based methods
      1. User-based collaborative filtering
      2. Item-based collaborative filtering
    2. Model-based methods
  2. Content-based recommender methods 
  3. Knowledge-based recommender systems
  4. Hybrid systems
All of them are based on:
  • Agent interactions – dynamic or time oriented
  • Item attributes – static or features oriented
  • Ratings matrices

Thursday, December 21, 2017

Recommender Systems


Recommender systems are, after all, utilized by merchants to increase their profit. In order to achieve the broader business-centric goal of increasing revenue, the common operational and technical goals of recommender systems are as follows:
  1. Relevance: The most obvious operational goal of a recommender system is to recommend items that are relevant to the user at hand. Users are more likely to consume items they find interesting. Although relevance is the primary operational goal of a recommender system, it is not sufficient in isolation. Therefore, we discuss several secondary goals below, which are not quite as important as relevance but are nevertheless important enough to have a significant impact.
  2. Novelty: Recommender systems are truly helpful when the recommended item is something that the user has not seen in the past. For example, popular movies of a preferred genre would rarely be novel to the user. Repeated recommendation of popular items can also lead to reduction in sales diversity.
  3. Serendipity: A related notion is that of serendipity [229], wherein the items recommended are somewhat unexpected, and therefore there is a modest element of lucky discovery, as opposed to obvious recommendations. Serendipity is different from novelty in that the recommendations are truly surprising to the user, rather than simply something they did not know about before. It may often be the case that a particular user may only be consuming items of a specific type, although a latent interest in items of other types may exist which the user might themselves find surprising. Unlike novelty, serendipitous methods focus on discovering such recommendations. For example, if a new Indian restaurant opens in a neighbourhood, then the recommendation of that restaurant to a user who normally eats Indian food is novel but not necessarily serendipitous. On the other hand, when the same user is recommended Ethiopian food, and it was unknown to the user that such food might appeal to her, then the recommendation is serendipitous. Serendipity has the beneficial side effect of increasing sales diversity or beginning a new trend of interest in the user. Increasing serendipity often has long-term and strategic benefits to the merchant because of the possibility of discovering entirely new areas of interest. On the other hand, algorithms that provide serendipitous recommendations often tend to recommend irrelevant items. In many cases, the longer term and strategic benefits of serendipitous methods outweigh these short-term disadvantages.
  4. Increasing recommendation diversity: Recommender systems typically suggest a list of top-k items. When all these recommended items are very similar, it increases the risk that the user might not like any of these items. On the other hand, when the recommended list contains items of different types, there is a greater chance that the user might like at least one of these items. Diversity has the benefit of ensuring that the user does not get bored by repeated recommendation of similar items.

Friday, December 15, 2017

Process mining as the bridge between data science and process science


Process science is an umbrella term for the broader discipline that combines knowledge from information technology and knowledge from management sciences to improve and run operational processes


The ingredients contributing to data science


Data science

Data science is an interdisciplinary field aiming to turn data into real value. Data may be structured or unstructured, big or small, static or streaming. Value may be provided in the form of predictions, automated decisions, mod- els learned from data, or any type of data visualization delivering insights. Data science includes data extraction, data preparation, data exploration, data transformation, storage and retrieval, computing infrastructures, var- ious types of mining and learning, presentation of explanations and pre- dictions, and the exploitation of results taking into account ethical, social, legal, and business aspects.

The above definition implies that data science is broader than applied statistics and data mining. Data scientists assist organizations in turning data into value. A data scientist can answer a variety of data-driven questions. These can be grouped into the following four main categories:

  • (Reporting) What happened?
  • (Diagnosis) Why did it happen?
  • (Prediction) What will happen?
  • (Recommendation) What is the best that can happen?

An example of a customer journey illustrating the many (digital) touchpoints generating events that allow us to understand and serve customers better


Internet of Events


Tuesday, October 31, 2017

Computer Science Bibliography


  1. Introduction to Algorithms - by Thomas H. Cormen 
  2. Structure and Interpretation of Computer Programs - by Harold Abelson 
  3. The C Programming Language - by Brian W. Kernighan 
  4. The Pragmatic Programmer: From Journeyman to Master - by Andy Hunt
  5. The Art of Computer Programming, Volumes 1-3 Boxed Set by Donald Ervin Knuth 
  6. Design Patterns: Elements of Reusable Object-Oriented Software by Erich Gamma 
  7. Introduction to the Theory of Computation by Michael Sipser 
  8. Code: The Hidden Language of Computer Hardware and Software by Charles Petzold 
  9. The Mythical Man-Month: Essays on Software Engineering by Frederick P. Brooks Jr. 
  10. Artificial Intelligence: A Modern Approach by Stuart Russell 
  11. Code Complete by Steve McConnell 
  12. The Protocols (TCP/IP Illustrated, Volume 1) by W. Richard Stevens 
  13. Algorithms by Robert Sedgewick 
  14. Advanced Programming in the UNIX Environment by W. Richard Stevens 
  15. A Discipline of Programming by Edsger W. Dijkstra 
  16. Introduction to Automata Theory, Languages, and Computation by John E. Hopcroft 
  17. Compilers: Principles, Techniques, and Tools by Alfred V. Aho 
  18. Learn You a Haskell for Great Good!: A Beginner's Guide by Miran Lipovača 
  19. The Society of Mind by Marvin Minsky 
  20. Concrete Mathematics: A Foundation for Computer Science by Ronald L. Graham 
  21. An Introduction to Functional Programming Through Lambda Calculus by Greg Michaelson 
  22. The STREAM TONE: The Future of Personal Computing? by T. Gilling 
  23. Fundamental Kotlin by Miloš Vasić
  24. The Algorithm Design Manual by Steven S. Skiena 
  25. Programming Pearls by Jon L. Bentley 
  26. The Elements of Computing Systems: Building a Modern Computer from First Principles by Noam Nisan 
  27. The Psychology of Computer Programming by Gerald M. Weinberg
  28. Applied Cryptography: Protocols, Algorithms, and Source Code in C by Bruce Schneier 
  29. Hacker's Delight by Henry S. Warren Jr. 
  30. Database System Concepts by Abraham Silberschatz 
  31. A First Course in Logic: An Introduction to Model Theory, Proof Theory, Computability, and Complexity by Shawn Hedman 
  32. Computer Systems: A Programmer's Perspective by Randal E. Bryant 
  33. Basic Proof Theory by Anne S. Troelstra 
  34. Structured Computer Organization by Andrew S. Tanenbaum 
  35. Quality Software Management: Systems Thinking by Gerald M. Weinberg 
  36. Computability and Logic by George S. Boolos 
  37. Waltzing with Bears: Managing Risk on Software Projects by Tom DeMarco 
  38. An Introduction to Database Systems by C.J. Date 
  39. Chaos: Making a New Science by James Gleick 
  40. What Is Life? with Mind and Matter and Autobiographical Sketches by Erwin Schrödinger 
  41. The Annotated Turing: A Guided Tour Through Alan Turing's Historic Paper on Computability and the Turing Machine by Charles Petzold 
  42. Feynman Lectures On Computation by Richard Feynman 
  43. Computational Complexity by Christos H. Papadimitriou 
  44. The Fractal Geometry of Nature by Benoît B. Mandelbrot 
  45. Exploring Requirements: Quality Before Design by Donald C. Gause 
  46. The It Handbook for Business: Managing Information Technology Support Costs by William C. Couie 
  47. Six Degrees: The Science of a Connected Age by Duncan J. Watts 
  48. Computability and Unsolvability by Martin D. Davis 
  49. Communication Networks: Fundamental Concepts and Key Architectures by Alberto Leon-Garcia 
  50. Computability Theory by S. Barry Cooper 
  51. Journey through Genius: The Great Theorems of Mathematics by William Dunham 
  52. The Quark and the Jaguar: Adventures in the Simple and the Complex by Murray Gell-Mann 
  53. Sync: The Emerging Science of Spontaneous Order by Steven H. Strogatz 
  54. How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life by Albert-László Barabási 
  55. Engines of Creation: The Coming Era of Nanotechnology by K. Eric Drexler 
  56. Refactoring: Improving the Design of Existing Code by Martin Fowler 
  57. Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency by Tom DeMarco 
  58. Elements of the Theory of Computation by Harry R. Lewis 
  59. Lambda-Calculus and Combinators: An Introduction by J. Roger Hindley 
  60. Lambda-Calculus, Combinators and Functional Programming by György E. Révész 
  61. Design and Validation of Computer Protocols by Gerard J. Holzmann 
  62. File Structures: An Object-Oriented Approach with C++ by Michael J. Folk 
  63. Debugging: The 9 Indispensable Rules for Finding Even the Most Elusive Software and Hardware Problems by David J. Agans
  64. The Meme Machine by Susan Blackmore 
  65. Does God Play Dice?: The New Mathematics of Chaos by Ian Stewart 
  66. The Strangest Man: The Hidden Life of Paul Dirac, Mystic of the Atom by Graham Farmelo 
  67. The Hidden Connections: A Science for Sustainable Living by Fritjof Capra 
  68. Purely Functional Data Structures by Chris Okasaki 
  69. The Calculus of Computation: Decision Procedures with Applications to Verification by Aaron R. Bradley 
  70. Cracking the Coding Interview: 150 Programming Questions and Solutions by Gayle Laakmann McDowell 
  71. Modern Operating Systems by Andrew S. Tanenbaum 
  72. Algorithm Design by Jon Kleinberg 
  73. Source Code Optimization Techniques For Data Flow Dominated Embedded Software by Heiko Falk 
  74. Advanced Compiler Design and Implementation by Steven S. Muchnick 
  75. A Practical Introduction to Computer Architecture by Daniel Page 
  76. Patterns of Enterprise Application Architecture by Martin Fowler 
  77. The Science of Liberty: Democracy, Reason and the Laws of Nature by Timothy Ferris 
  78. Reinventing Discovery: The New Era of Networked Science by Michael Nielsen
  79. On Growth and Form by D'Arcy Wentworth Thompson 
  80. Cycles of Time: An Extraordinary New View of the Universe by Roger Penrose 
  81. Chances Are . . .: Adventures in Probability by Michael Kaplan 
  82. Angels and Ages: A Short Book About Darwin, Lincoln, and Modern Life by Adam Gopnik 
  83. Total Recall: How the E-Memory Revolution Will Change Everything by C. Gordon Bell 
  84. Annoying: The Science of What Bugs Us by Joe Palca 
  85. Collider: The Search for the World's Smallest Particles by Paul Halpern
  86. Denialism: How Irrational Thinking Hinders Scientific Progress, Harms the Planet, and Threatens Our Lives by Michael Specter 
  87. Six Impossible Things Before Breakfast: The Evolutionary Origins of Belief 
  88. by Lewis Wolpert 
  89. The Pleasure Instinct: Why We Crave Adventure, Chocolate, Pheromones, and Music 
  90. by Gene Wallenstein 
  91. Shadows of the Mind: A Search for the Missing Science of Consciousness by Roger Penrose 
  92. The Age of Entanglement: When Quantum Physics Was Reborn by Louisa Gilder 
  93. The Clockwork Universe: Isaac Newton, the Royal Society, and the Birth of the Modern World by Edward Dolnick 
  94. Quantum Man: Richard Feynman's Life in Science by Lawrence M. Krauss 
  95. The Day We Found the Universe by Marcia Bartusiak 
  96. A New Kind of Science by Stephen Wolfram 
  97. Letters to a Young Mathematician by Ian Stewart 
  98. A History of π by Petr Beckmann 
  99. Out of Control: The New Biology of Machines, Social Systems, and the Economic World by Kevin Kelly 
  100. The User Illusion: Cutting Consciousness Down to Size by Tor Nørretranders 
  101. Programming the Universe: A Quantum Computer Scientist Takes on the Cosmos by Seth Lloyd 
  102. Biology as Ideology: The Doctrine of DNA by Richard C. Lewontin 
  103. The End of Time: The Next Revolution in Our Understanding of the Universe by Julian Barbour 
  104. A World Without Time: The Forgotten Legacy of Gödel And Einstein by Palle Yourgrau 
  105. Dreams of a Final Theory: The Scientist's Search for the Ultimate Laws of Nature by Steven Weinberg 
  106. The Trouble with Physics: The Rise of String Theory, the Fall of a Science and What Comes Next by Lee Smolin 
  107. Your Brain at Work: Strategies for Overcoming Distraction, Regaining Focus, and Working Smarter All Day Long by David Rock 
  108. Unequal Protection: The Rise of Corporate Dominance and the Theft of Human Rights by Thom Hartmann 
  109. Poor Charlie's Almanack: The Wit and Wisdom of Charles T. Munger by Charles T. Munger 
  110. Eight Little Piggies: Reflections in Natural History by Stephen Jay Gould 
  111. The Smart Swarm: How Understanding Flocks, Schools, and Colonies Can Make Us Better at Communicating, Decision Making, and Getting Things Done by Peter Miller 
  112. Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson 
  113. The Body Has a Mind of Its Own: How Body Maps in Your Brain Help You Do (Almost) Everything Better by Sandra Blakeslee 
  114. The Age of Empathy: Nature's Lessons for a Kinder Society by Frans de Waal 
  115. Wholeness and the Implicate Order by David Bohm 
  116. The Structure of Evolutionary Theory by Stephen Jay Gould 
  117. The Evolution of Cooperation by Robert Axelrod 
  118. Philosophy in the Flesh: The Embodied Mind and its Challenge to Western Thought by George Lakoff 
  119. Quantum: Einstein, Bohr and the Great Debate About the Nature of Reality by Manjit Kumar 
  120. The Theory of Almost Everything: The Standard Model, the Unsung Triumph of Modern Physics by Robert Oerter 
  121. Seeking Wisdom: From Darwin To Munger by Peter Bevelin 
  122. The Master and His Emissary: The Divided Brain and the Making of the Western World by Iain McGilchrist 
  123. Complexity, adaptive leadership, phase transitions, and new emergent order: A case study of the Northwest Texas Conference of the United Methodist Church. by Bryan D. Sims 
  124. Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation by Michael J. North 
  125. The Emperor's Nightingale: How the Emerging Dynamics of Corporate Complexity Will Restore Life in the New Millennium by Robert A.G. Monks
  126. Machines, Languages, And Complexity: 5th International Meeting Of Young Computer Scientists, Smolenice, Czechoslovakia, November 14 18, 1988: Selected Contributions by Jürgen Dassow
  127. Regional Pathways to Complexity: Settlement and Land-Use Dynamics in Early Italy from the Bronze Age to the Republican Period by Peter Attema 
  128. Nonlinear Dynamics of Chaotic and Stochastic Systems: Tutorial and Modern Developments. Springer Complexity, Springer Series in Synergetics. by Vadim S. Anishchenko 
  129. Complexity Theory and the Management of Networks: Proceedings of the Workshop on Organisational Networks as Distributed Systems of Knowledge by Pierpaolo Andriani
  130. The New Economy and Macroeconomic Stability: A Neo-Modern Perspective Drawing on the Complexity Approach and Keynesian Economics by Dario Togati 
  131. Applications of Automata Theory and Algebra: Via the Mathematical Theory of Complexity to Biology, Physics, Psychology, Philosophy, and Games by John L. Rhodes 
  132. The Effective Organization: Practical Application of Complexity Theory and Organizational Design to Maximize Performance in the Face of Emerging Events by Dennis A. Tafoya 
  133. AI Approaches to the Complexity of Legal Systems: Complex Systems, the Semantic Web, Ontologies, Argumentation, and Dialogue by Pompeu Casanovas 
  134. Resource Efficiency Complexity and the Commons: The Paracommons and Paradoxes of Natural Resource Losses, Wastes and Wastages by Bruce Lankford 
  135. On the (Im)Possibility of Business Ethics: Critical Complexity, Deconstruction, and Implications for Understanding the Ethics of Business by Minka Woermann  

Common Exponential Distributions and their Sufficient Statistics


Thursday, August 31, 2017

Mito platônico descrito na República

A idéia é a seguinte: é descrita uma situação na qual  escravos são criados desde o seu nascimento acorrentados em uma caverna de tal modo que jamais possam virar-se para trás e a única coisa que vêem consiste em sombras projetadas na parede em sua frente, relativas à luz de uma fogueira localizada atrás dos mesmos. Para estes escravos, portanto, o mundo é composto apenas por estas projeções e nada mais.

Um belo dia, um dos escravos se solta das algemas e, tateando as paredes da caverna, acaba por subir à superfície. Ao se deparar com o mundo exterior, primeiro é ofuscado pela luz do Sol. Em seguida, conforme vai se acostumando à luz, começa a observar objetos que jamais havia sonhado: árvores, grama, animais, casas, para seu espanto outros homens e, finalmente, o céu. Óbviamente, nosso escravo fica maravilhado. E, de tão maravilhado que fica, decide voltar à caverna para contar a seus companheiros a novidade. Ao fazê-lo, primeiro é tido como louco para, em seguida, ser assassinado pelos mesmos.

Tuesday, August 22, 2017

Computational intelligence

Computational intelligence as the computational models and tools of intelligence capable of inputting raw numerical sensory data directly, processing them by exploiting the representational parallelism and pipelining of the problem, generating reliable & timely responses and withstanding high fault tolerance.


Friday, August 18, 2017

Computational Intelligence

A system is computationally intelligent when it: deals with only numerical (low level) data, has pattern recognition components, does not use knowledge in the AI sense: and additionally when it (begins to) exhibit i) computational adaptivity, ii) computational fault tolerance, Hi) speed approaching human-like turnaround and iv) error rates that approximate human performance.



  • PR= Probabilistic reasoning
  • BN= Belief networks
  • FL= Fuzzy logic
  • NN= Neural nets
  • GA= Genetic algorithms
  • EP= Evolutionary programming
  • AL= Artificial life