The Present and Future
              of
Personalized Recommendations


      Kartik Hosanagar
Personalized Retail
Personalized Radio
Personalized News
3rd Party Providers




                      Img Src: Business 2.0
Why are Recommenders Important?
Recommenders can be beneficial to both consumers and firms.


          Value to    • Lear...
Recommender Design
 Content Based              Collaborative        Social Network


                 Correlation Engine  ...
The Amazon Approach
Amazon uses all available information about customers in order to present the
most relevant offer poss...
The Netflix Approach
Utilizing customer information, Netflix is able to improve profits through its
recommendation engine....
Cinematch Engine
 Cinematch uses statistical techniques to identify similar users and recommend
 movies based on ratings o...
Recommender Impact: Substitution
or Incremental Sales?
Impact on Volume
(Fleder & Hosanagar 2007)
             45

                                  Monthly
Songs
Added
(Median)...
Recommenders => Long Tail?
(Fleder & Hosanagar 2008)




•  Do recommenders (collaborative filters) foster
   discovery of...
Results
1.  Collaborative filters can help enhance sales diversity
    (e.g., by increasing awareness) but …

        a de...
Consumer level effects
•    Individual diversity can increase but aggregate
     diversity decreases




•    Basic design...
Why do Recommenders Work?
(Fleder & Hosanagar 2007)
•  Lots of biases in
   –  What people watch
   –  What people rate
• ...
Results




                       Random    NR (a)        NR (b)     NR (c)     NR (d)

Chance missing         Equal    I...
Future of Personalized
Recommendations?
•  Discovery
•  Fluid inter-site personalization
   –  Privacy
   –  Ownership



...
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Hosanagar Supernova 2008

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Hosanagar Supernova 2008

  1. 1. The Present and Future of Personalized Recommendations Kartik Hosanagar
  2. 2. Personalized Retail
  3. 3. Personalized Radio
  4. 4. Personalized News
  5. 5. 3rd Party Providers Img Src: Business 2.0
  6. 6. Why are Recommenders Important? Recommenders can be beneficial to both consumers and firms. Value to • Learn about new products Consumers: • Sort through myriad choices Value to • Convert browser to buyers Firms: • Cross-sell • Increase loyalty Used by major Internet firms (Amazon, NetFlix, Yahoo!)
  7. 7. Recommender Design Content Based Collaborative Social Network Correlation Engine Recommendations vs. High Correlation
  8. 8. The Amazon Approach Amazon uses all available information about customers in order to present the most relevant offer possible. Customer views an item A bundle is created based on the look-alikes Amazon immediately identifies his profile and recent history, the product attributes and the Additional items are proposed, behavior of similar customers based on other customers’ buying behavior
  9. 9. The Netflix Approach Utilizing customer information, Netflix is able to improve profits through its recommendation engine. Based on the “persona”, a group Customer asked to rate movies of movies the Customer will Movie ratings are used by Netflix to probably like is created build the customer “persona” Based on the ratings provided by customer with a similar A movie is then recommended “persona”
  10. 10. Cinematch Engine Cinematch uses statistical techniques to identify similar users and recommend movies based on ratings of similar users. Dataset Correlation Engine Recommendations 100 Million 18,000 vs. User Ratings Movies High Correlation  Encourage users to rate  Determine correlations  Recommend movies in user ratings to movies based identify similar users on evaluations of similar people NetFlix Data Available (NetFlix Challenge)
  11. 11. Recommender Impact: Substitution or Incremental Sales?
  12. 12. Impact on Volume (Fleder & Hosanagar 2007) 45
 Monthly
Songs
Added
(Median)
by
User
Type
 40
 35
 30
 25
 iLike
Users
 Number

20
 of

 15
 Songs
 control
 10
 5
 0
 December
 January
 February
 March
 April
 May

 June
 July
 (2006)
 iLike
Users
 15
 14
 12
 41
 27
 23
 21
 18
 Control
 10
 9
 7
 8
 8
 10
 7
 N/A Results available in popular press(Billboard, Yahoo)
  13. 13. Recommenders => Long Tail? (Fleder & Hosanagar 2008) •  Do recommenders (collaborative filters) foster discovery of obscure/niche items?
  14. 14. Results 1.  Collaborative filters can help enhance sales diversity (e.g., by increasing awareness) but … a design feature, namely the use of sales data to recommend products, can often come in the way and drive up sales concentration 14
  15. 15. Consumer level effects •  Individual diversity can increase but aggregate diversity decreases •  Basic design choices affect the outcome How do you foster discovery? Results available on web (SSRN)
  16. 16. Why do Recommenders Work? (Fleder & Hosanagar 2007) •  Lots of biases in –  What people watch –  What people rate •  Most systems assume ratings missing at random … yet they work. Why? •  Test the impact of missing ratings
  17. 17. Results Random NR (a) NR (b) NR (c) NR (d) Chance missing Equal Increasing Decreasing U-Shape Inverse-U Prediction error (E) 0.770 0.785 0.791 0.945 0.686
  18. 18. Future of Personalized Recommendations? •  Discovery •  Fluid inter-site personalization –  Privacy –  Ownership Contact: kartikh@wharton.upenn.edu
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