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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)