Closing Case of Chapter 6:Just How Predictable Are You?

Presenter: Hongyeon Lee

February 25, 2012

Topics to be covered

1. Key Problems or Issues2. IT Solutions or Alternatives3. Results 4. Case Questions and Answers

1. Key Problems or Issues

• Allow the company to pinpoint your tastes and determine the likelihood that you will buy a given product

1. Key Problems or Issues (continued)

• Companies in the rec-ommendation business maintain the Web is leaving the era of search and entering the era of discovery

1. Key Problems or Issues (continued)

• Building a personalized discovery mechanismmeans tapping into all the manners of expression, categorization, and opin-ions that exist on the Web today

2. IT Solutions or Alternatives

• Amazon uses a series of collaborative filtering algo-rithms to predict which products you will buy by analyzing your purchasing patterns

2. IT Solutions or Alternatives (continued)

• By rating songs and artists, you can refine the sugges-tions, allowing Pandora to create a truly personalized music collection for you

3. Results

• Google presumably has a recommendation ap-plication in the works

4. Case Question No. 1

What are the implications of recommenders? What is the relationship between your pri-vacy and recommendation engines? Are rec-ommendation engines the ultimate form of 1:1, or personalized, marketing?

4. Answer to Case Question No. 1

• Recommenders can introduce you to a lot of preferences and tastes through personality-based advertising

• There is a trade off between the accuracy of the recom-mendation and the privacy of the network

• Recommendation engines will pursuit a combination of 1:1 or personalized marketing and customization market-ing

4. Case Question No. 2

What are the implications for a recom-mender like Pandora with regard to copyright violation?

4. Answer to Case Question No. 2

• The recommender should perform its policy to disable and terminate the accounts of users who are repeatedly charged with in-fringing copyrights of any other entity

Q & A Session

Thank you

Definition of Terms

• Recommender systems or recommendation sys-tems (sometimes called as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item or social ele-ment they had not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user's social environ-ment (collaborative filtering approaches)