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Google Tech Talk:  Social Recommendations April 10, 2008, 1 p.m. Presented by Dan Carroll of
Introduction <ul><li>Social recommendations, a new lens </li></ul><ul><li>Hyper-relevant results delivered through hierarc...
Introduction <ul><li>Information age: access v. experience  </li></ul><ul><li>Design principle: respect for the beauty, co...
Overview <ul><li>Recommendations today </li></ul><ul><li>Benefits and limitations </li></ul><ul><li>General concepts of so...
Recommendations today <ul><li>Provide access  </li></ul><ul><li>Play a critical role in our lives </li></ul>
Examples <ul><li>What to read: </li></ul>What to hear:
More Examples <ul><li>What to watch: </li></ul>What is news:
More Examples <ul><li>Where to Eat: </li></ul>Where to work:
More Examples <ul><li>Who to date: </li></ul>Who to marry:
What works <ul><li>Current search engines provide examples of successful recommendations based on spidered data and reques...
What also works <ul><li>Amazon, Netflix and iTunes provide examples of successful recommendations based on the ratings of ...
Limitations <ul><li>Tyranny of the bored (Bill Goldsmith of Radio Paradise) </li></ul><ul><li>Leave Brittany  Alone on You...
Monotony of the masses
The pigeon hole
What’s next <ul><li>Social recommendation assumes </li></ul><ul><li>Friends and other connections are trusted sources of i...
Why is this important
Why is this important <ul><li>We’re designing future experiences. </li></ul>
Assumptions <ul><li>Access-based recommenders (i.e. search engines) assume you know what you want and you’re willing to ta...
Challenges <ul><li>Privacy </li></ul><ul><li>Control </li></ul><ul><li>Relevance v. permanence </li></ul>
Current efforts <ul><li>YouTube </li></ul><ul><li>Facebook’s friend recommender </li></ul><ul><li>TrustedOpinion </li></ul...
What’s next <ul><li>Curated experience </li></ul><ul><li>Socializing the web </li></ul>
Amazon socialized <ul><li>Discovery may indeed be for web 2.0 what search was for web 1.0. </li></ul>
Yelp socialized
YouTube socialized
What’s needed <ul><li>An initiative that ties together the social graph, relationship data from closed social networks and...
Opportunity <ul><li>Utilization of friend of friend and usage data to make intelligent recommendations based on actions an...
SoMR: Social Media Recommendation API <ul><li>Algorithm to generate recommendations by social relevancy </li></ul><ul><li>...
Current project
New genre (geo-location)
New social media experiences
SoMR design principles <ul><li>Beauty </li></ul><ul><li>Complexity </li></ul><ul><li>Diversity </li></ul><ul><li>Evolution...
SoMR technical  design elements <ul><li>Influence as primary metric </li></ul><ul><li>Dynamic formula </li></ul><ul><li>So...
<ul><li>Build superior media applications  </li></ul><ul><li>Provide lift for advertising networks </li></ul><ul><li>Add s...
<ul><li>Mashed-up applications </li></ul><ul><li>Cross-platform social intelligence  </li></ul><ul><li>Social relevance wi...
<ul><li>Build something –  [email_address]   </li></ul><ul><li>Optimize Advertising –  [email_address] </li></ul><ul><li>O...
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Google Tech Talk on Social Recommendation

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This slide show was a companion to the Google Tech talk on Social Recommendation give on April 10, 2008.

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  • Transcript of "Google Tech Talk on Social Recommendation"

    1. 1. Google Tech Talk: Social Recommendations April 10, 2008, 1 p.m. Presented by Dan Carroll of
    2. 2. Introduction <ul><li>Social recommendations, a new lens </li></ul><ul><li>Hyper-relevant results delivered through hierarchical social relationships </li></ul>
    3. 3. Introduction <ul><li>Information age: access v. experience </li></ul><ul><li>Design principle: respect for the beauty, complexity, diversity and evolution of the human condition </li></ul>
    4. 4. Overview <ul><li>Recommendations today </li></ul><ul><li>Benefits and limitations </li></ul><ul><li>General concepts of social recommendation </li></ul><ul><li>Why it is important? </li></ul><ul><li>What are the opportunities? </li></ul><ul><li>What are the challenges? </li></ul><ul><li>Current efforts in social recommendation </li></ul><ul><li>SoMR, the Social Media Recommendation engine </li></ul><ul><li>Questions </li></ul>
    5. 5. Recommendations today <ul><li>Provide access </li></ul><ul><li>Play a critical role in our lives </li></ul>
    6. 6. Examples <ul><li>What to read: </li></ul>What to hear:
    7. 7. More Examples <ul><li>What to watch: </li></ul>What is news:
    8. 8. More Examples <ul><li>Where to Eat: </li></ul>Where to work:
    9. 9. More Examples <ul><li>Who to date: </li></ul>Who to marry:
    10. 10. What works <ul><li>Current search engines provide examples of successful recommendations based on spidered data and requested information. </li></ul>
    11. 11. What also works <ul><li>Amazon, Netflix and iTunes provide examples of successful recommendations based on the ratings of their users. </li></ul>
    12. 12. Limitations <ul><li>Tyranny of the bored (Bill Goldsmith of Radio Paradise) </li></ul><ul><li>Leave Brittany Alone on YouTube </li></ul>
    13. 13. Monotony of the masses
    14. 14. The pigeon hole
    15. 15. What’s next <ul><li>Social recommendation assumes </li></ul><ul><li>Friends and other connections are trusted sources of information. </li></ul><ul><li>Social relevancy can provide a superior experience. </li></ul>
    16. 16. Why is this important
    17. 17. Why is this important <ul><li>We’re designing future experiences. </li></ul>
    18. 18. Assumptions <ul><li>Access-based recommenders (i.e. search engines) assume you know what you want and you’re willing to take the time to find it. </li></ul><ul><li>Social recommendations can enhance access-based recommenders or create a completely new experience. </li></ul>
    19. 19. Challenges <ul><li>Privacy </li></ul><ul><li>Control </li></ul><ul><li>Relevance v. permanence </li></ul>
    20. 20. Current efforts <ul><li>YouTube </li></ul><ul><li>Facebook’s friend recommender </li></ul><ul><li>TrustedOpinion </li></ul><ul><li>Social Suggester </li></ul><ul><li>Stumble Upon </li></ul><ul><li>MyStrands </li></ul>
    21. 21. What’s next <ul><li>Curated experience </li></ul><ul><li>Socializing the web </li></ul>
    22. 22. Amazon socialized <ul><li>Discovery may indeed be for web 2.0 what search was for web 1.0. </li></ul>
    23. 23. Yelp socialized
    24. 24. YouTube socialized
    25. 25. What’s needed <ul><li>An initiative that ties together the social graph, relationship data from closed social networks and content identifiers. </li></ul><ul><li>Person to person relationships and person to content relationships could follow users throughout the sites they visit. </li></ul><ul><li>Contact dan@somr.org </li></ul>
    26. 26. Opportunity <ul><li>Utilization of friend of friend and usage data to make intelligent recommendations based on actions and interactions </li></ul><ul><li>Recommendation becomes curated experience </li></ul>
    27. 27. SoMR: Social Media Recommendation API <ul><li>Algorithm to generate recommendations by social relevancy </li></ul><ul><li>Web service for social recommendation </li></ul><ul><ul><li>Delivers superior content and advertising </li></ul></ul><ul><ul><li>Creates new social media experiences </li></ul></ul>
    28. 28. Current project
    29. 29. New genre (geo-location)
    30. 30. New social media experiences
    31. 31. SoMR design principles <ul><li>Beauty </li></ul><ul><li>Complexity </li></ul><ul><li>Diversity </li></ul><ul><li>Evolution </li></ul>Designers of applications, social networks and content sites need to be focused on social interactions and human-centric responses which respect these human qualities.
    32. 32. SoMR technical design elements <ul><li>Influence as primary metric </li></ul><ul><li>Dynamic formula </li></ul><ul><li>Social relationships, activity, implicit and explicit </li></ul><ul><li>Supplemental not substitutional </li></ul>
    33. 33. <ul><li>Build superior media applications </li></ul><ul><li>Provide lift for advertising networks </li></ul><ul><li>Add social relevance to existing applications, platforms and sites </li></ul>SoMR API allows:
    34. 34. <ul><li>Mashed-up applications </li></ul><ul><li>Cross-platform social intelligence </li></ul><ul><li>Social relevance within your content site </li></ul>What will you build?
    35. 35. <ul><li>Build something – [email_address] </li></ul><ul><li>Optimize Advertising – [email_address] </li></ul><ul><li>Or just find out more – somr.org </li></ul><ul><li>Thanks! Dan Carroll [email_address] .org </li></ul>Interested? Then,
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