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Long Tail by Chris Anderson. Hassan Sayyadi [email protected] Touching the Void Phenomenon. Touching the Void , a harrowing account of near death in the Peruvian Andes Joe Simpson, 1988 Into Thin Air Jon Krakauer, 1997. Touching the Void Phenomenon: How?.

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touching the void phenomenon
Touching the Void Phenomenon
  • Touching the Void, a harrowing account of

near death in the Peruvian Andes

    • Joe Simpson, 1988
  • Into Thin Air
    • Jon Krakauer, 1997
touching the void phenomenon how
Touching the Void Phenomenon: How?
  • Amazon.com recommendations and online booksellers\' software
    • From the pattern in buying behavior
  • People took the recommendation
    • More sales
  • more algorithm-fueled recommendations

the positive feedback loop

touching the void phenomenon why
Touching the Void Phenomenon: Why?
  • Hit-driven economy
    • Two limitation of physical world
      • The need to find local audience
      • The physics itself
    • But are hits enough for everybody?
  • Online retails
    • A world of abundance
    • Solve the problem of long tail
online retailer examples music
Online Retailer Examples: Music
  • Rhapsody vs. Walmart
    • Walmart
      • Carry a title if it sells at least 100,000 copies
      • Less than 1 percent of CDs do that kind of volume
    • Rhapsody
      • Power low demand
      • Hits for tracks below top 40,000 even 400,000 tracks
online retailer examples books and movies
Online Retailer Examples: Books and Movies
  • Amazon vs. Barnes & Noble
    • Barnes & Noble
      • Carries 13,000 titles
    • Amazon
      • More than half of its sales come from outside its top 13,000 titles
  • Netflix vs. Blockbuster
    • Blockbuster: Carries 3,000 DVDs
    • Netflix: a fifth of Netflix rentals are outside its top 3,000 titles
rules for the new entertainment economy
Rules for the new entertainment economy
  • Rule 1: Make everything available
    • Don’t look at physical shelves for an item in the long tail
    • Online retailers can aggregate dispersed audience
    • Almost anything is worth offering on the off chance it will find a buyer.
      • The opposite of the way the entertainment industry now thinks.
rules for the new entertainment economy1
Rules for the new entertainment economy
  • Rule 2: Cut the price in half. Now lower it
    • No cost of the retail channel - CD manufacturing, distribution, and retail overheads
    • When you lower prices, people tend to buy more
      • Rhapsody experiment:
        • The service offered tracks at 99, 79, and 49 cents
        • Although the 49-cent tracks were only half the price of the 99-cent tracks, it sold three times as many of them.
  • Future Market
    • On-demand and free to listeners and advertising-supported, like radio
rules for the new entertainment economy2
Rules for the new entertainment economy
  • Rule 3: Help me find it
    • MP3.com
      • Only long tail
        • No familiar point of entry for consumers, no known quantity from which further exploring could begin
      • Need both ends of the curve
    • How to find
      • Rhapsody: a combination of human editors and genre guides
      • Amazon: collaborative filtering
    • Netflix: 60 percent of rentals come from recommendations
motivation
Motivation
  • Intel website redesign
    • Added a verbal advisor for digital camera drivers.

 Successful download increased 27%.

  • But Intel may increase downloads even more
    • Different users prefer different characteristics

 Automatically change website characteristics for each users

website design selection issues
Website Design Selection Issues
  • Solutions
    • Personalized self-selection
    • Cognitive-style surveys
  • Difficulties
    • Complex, confusing websites which are difficult to use
    • Too cumbersome and intrusive for retail website visitors
website morphing
Website Morphing
  • Morphing the website automatically by matching characteristics to costumers’ cognitive style.
  • Challenges
    • First-time visitors
    • Learning best website characteristics for each cognitive-style
    • Finding prior distributions
    • Real-time working
cognitive style inference
Cognitive-Style Inference

Each visitor’s click is a decision point that reveals the visitor’s cognitive style preferences.

Posterior distribution

na ve morph assignment
Naïve Morph Assignment
  • For cognitive style r:
    • a_i = the number of users who bought the plan after assigning morph i
    • b_i =the number of users who did not buy a plan aftre assigning morph i
    • Best Morph
      • The morph i which maximize {a_i / (a_i + b_i)}
optimal morph assignment
Optimal Morph Assignment
  • Exploration VS Exploitation
  • Reward: Immediate reward+ discounted future reward
    • Immediate reward
      • Probability of selling an item times the profit earned by selling the item
    • Discounted future reward
      • expected reward of acting optimally in the future discounted by factor a.

 Dynamic Programming

optimal morph assignment1
Optimal Morph Assignment
  • Reward Function to maximize
  • G is a decreasing function of n
    • High uncertainty (small n): Exploration
    • Low uncertainty (large n): Exploitation
      • G converges to a/(a+b)
cognitive style uncertainty in optimal morph assignment
Cognitive-Style uncertainty in Optimal Morph Assignment
  • The Bayesian cognitive-style inference model gives a probability for each cognitive style.
    • Results in the uncertainty in the optimal morph assignment
  • Model update by replacing absolute values with expected values:
experiments cognitive style measures
Experiments: Cognitive-Style measures

Leader vs. follower

Analytic/visual vs. holistic/verbal

Impulsive vs. deliberative

(Active) reader vs. (passive) listener

experiments morph characteristics
Experiments: Morph Characteristics

Graphical vs. verbal

Small-load vs. large-load

Focused content vs. general content

estimation of click alternative preferences
Estimation of Click-Alternative preferences

Clickstream likelihood

Pair comparison likelihood

Cognitive-style preference posterior

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