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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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Long tail by chris anderson

Long Tailby Chris Anderson

Hassan Sayyadi

[email protected]

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

Website morphing by john r hauser glen l urban guilherme liberali and michael braun

Website Morphingby John R. Hauser, Glen L. Urban, Guilherme Liberali, and Michael Braun


  • 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