What your hairstyle says about your political preferences, and why you should care about the future of recommender systems

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Recent developments in the area of social networking have lead to prominent users leaving facebook due to privacy concerns.
In order to really understand what motivated facebook to implement these controversial changes, you have to look at the future of recommender systems. I will introduce my current research in the areas of multi-source, cross-domain and privacy enabled user profiling and recommendation,
and show how it relates to current developments in the social networking space.

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  • Start this slide by explaining the the major classes of rec. algo can be listed,
    in increasing expense of the amount of knowledge which they require:
    1.) CF is very cheap, only captures implicit knowledge
    2.) CB requires automated feature extraction which is an open research problem for e.g. music or movies
    3.) KB requires domain knowledge and annotations using this knowledge
    4.) hybrid algorithms are used to balance the knowledge cost of algorithms, e.g. CF + something else
  • Start this slide by explaining the the major classes of rec. algo can be listed,
    in increasing expense of the amount of knowledge which they require:
    1.) CF is very cheap, only captures implicit knowledge
    2.) CB requires automated feature extraction which is an open research problem for e.g. music or movies
    3.) KB requires domain knowledge and annotations using this knowledge
    4.) hybrid algorithms are used to balance the knowledge cost of algorithms, e.g. CF + something else
  • Start this slide by explaining the the major classes of rec. algo can be listed,
    in increasing expense of the amount of knowledge which they require:
    1.) CF is very cheap, only captures implicit knowledge
    2.) CB requires automated feature extraction which is an open research problem for e.g. music or movies
    3.) KB requires domain knowledge and annotations using this knowledge
    4.) hybrid algorithms are used to balance the knowledge cost of algorithms, e.g. CF + something else
  • Start this slide by explaining the the major classes of rec. algo can be listed,
    in increasing expense of the amount of knowledge which they require:
    1.) CF is very cheap, only captures implicit knowledge
    2.) CB requires automated feature extraction which is an open research problem for e.g. music or movies
    3.) KB requires domain knowledge and annotations using this knowledge
    4.) hybrid algorithms are used to balance the knowledge cost of algorithms, e.g. CF + something else
  • Start this slide by explaining the the major classes of rec. algo can be listed,
    in increasing expense of the amount of knowledge which they require:
    1.) CF is very cheap, only captures implicit knowledge
    2.) CB requires automated feature extraction which is an open research problem for e.g. music or movies
    3.) KB requires domain knowledge and annotations using this knowledge
    4.) hybrid algorithms are used to balance the knowledge cost of algorithms, e.g. CF + something else


  • Emphasise the story: How can a start-up with very limited number of users and data,
    go from their alpha phase to a competitive product? We have demonstrated that this is possible,
    by augmenting the data from a small closed corpus recommender system.

  • Facebooks ecosystem is actually based on open standards, however it is not open.
    It is a closed system.
    Finish with:
    Coming up with an alternative open system, and applying my research to such an open ecosystem,
    will be a way to evaluate the contributions of my PhD.









































  • What your hairstyle says about your political preferences, and why you should care about the future of recommender systems

    1. 1. Digital Enterprise Research Institute www.deri.ie What your hairstyle says about your political preferences, and why you should care about the future of recommender systems Benjamin Heitmann Unit for Information Mining and Retrieval (UIMR) Funded by Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Líon-2)  Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Chapter
    2. 2. Why has Facebook been so controversial? Digital Enterprise Research Institute www.deri.ie Benjamin.Heitmann slide 2 of 19 @deri.org
    3. 3. Why has Facebook been so controversial? Digital Enterprise Research Institute www.deri.ie  new Facebook features in April 2010  introduced new default privacy settings  from private to public by default Benjamin.Heitmann slide 2 of 19 @deri.org
    4. 4. Why has Facebook been so controversial? Digital Enterprise Research Institute www.deri.ie  new Facebook features in April 2010  introduced new default privacy settings  from private to public by default  huge backlash in the media Benjamin.Heitmann slide 2 of 19 @deri.org
    5. 5. Why has Facebook been so controversial? Digital Enterprise Research Institute www.deri.ie  new Facebook features in April 2010  introduced new default privacy settings  from private to public by default  huge backlash in the media  Result: simplified privacy defaults Benjamin.Heitmann slide 2 of 19 @deri.org
    6. 6. Why has Facebook been so controversial? Digital Enterprise Research Institute www.deri.ie  new Facebook features in April 2010  introduced new default privacy settings  from private to public by default  huge backlash in the media  Result: simplified privacy defaults  Why change defaults in the first place?  Advertisements are recommendations, they need user data! Benjamin.Heitmann slide 2 of 19 @deri.org
    7. 7. Recommendations have become a commodity! Digital Enterprise Research Institute www.deri.ie  Users expect “smart” web sites  Recommendations are part of such a user experience Benjamin.Heitmann slide 3 of 19 @deri.org
    8. 8. Recommendations have become a commodity! Digital Enterprise Research Institute www.deri.ie  Users expect “smart” web sites  Recommendations are part of such a user experience Benjamin.Heitmann slide 3 of 19 @deri.org
    9. 9. Recommendations have become a commodity! Digital Enterprise Research Institute www.deri.ie  Users expect “smart” web sites  Recommendations are part of such a user experience Benjamin.Heitmann slide 3 of 19 @deri.org
    10. 10. Recommendations have become a commodity! Digital Enterprise Research Institute www.deri.ie  Users expect “smart” web sites  Recommendations are part of such a user experience Benjamin.Heitmann slide 3 of 19 @deri.org
    11. 11. So why should you care? Digital Enterprise Research Institute www.deri.ie Benjamin.Heitmann slide 4 of 19 @deri.org
    12. 12. So why should you care? Digital Enterprise Research Institute www.deri.ie  Recommendations are a commodity: they are required for online products Benjamin.Heitmann slide 4 of 19 @deri.org
    13. 13. So why should you care? Digital Enterprise Research Institute www.deri.ie  Recommendations are a commodity: they are required for online products Facebook Open Graph protocol  coming paradigm shifts: recommendations outside the context of a single site Benjamin.Heitmann slide 4 of 19 @deri.org
    14. 14. So why should you care? Digital Enterprise Research Institute www.deri.ie  Recommendations are a commodity: they are required for online products Facebook Open Graph protocol  coming paradigm shifts: recommendations outside the context of a single site much more interesting / scary recommendations Benjamin.Heitmann slide 4 of 19 @deri.org
    15. 15. The challenge Digital Enterprise Research Institute www.deri.ie  Problem: How to compete with existing systems?  provide relevant results beyond your domain  share user profile data beyond your site  encourage users to trust you  Common aspect: vs.  multiple parties  eco-systems  architecture is new required to define startup established roles, standards and recommendation interaction services Benjamin.Heitmann slide 5 of 19 @deri.org
    16. 16. Overview Digital Enterprise Research Institute www.deri.ie  Background: How are recommendations made?  Multi-Source recommendations: Going beyond the context of a site  Privacy-enabled profiles: Enabling the post-facebook future  Cross-domain recommendations: So good, its almost scary Benjamin.Heitmann slide 6 of 19 @deri.org
    17. 17. Background: adaptive personalisation Digital Enterprise Research Institute www.deri.ie closed recommender system open recommender system input data input data recommendation recommendation algorithm algorithm background data integrated background data external data sources source 1 source n . . . .  Adaptive system: personalisation is based on an explicit user model and profile data  3 components: Rec. algorithm uses background and input data to make recommendations  Most existing rec. systems rely on collection of in-house data and do not use external data Benjamin.Heitmann slide 7 of 19 @deri.org
    18. 18. Multi-source recommendations: Digital Enterprise Research Institute www.deri.ie  First paradigm shift: use background data from external sources link their items to your users  extend the context of the recommendations beyond your site  Challenge: how to use structured data as recommendation input  Solution: use Linked Data Benjamin.Heitmann slide 8 of 19 @deri.org
    19. 19. Multi-source recommendations: Digital Enterprise Research Institute www.deri.ie  First paradigm shift: use background data from external sources link their items to your users  extend the context of the recommendations beyond your site  Challenge: how to use Linking the Facebook Social Graph to structured data as restaurants on Yelp recommendation input  Solution: use Linked Data Benjamin.Heitmann slide 8 of 19 @deri.org
    20. 20. Prototype: using Linked Data for multi- source recommendations Digital Enterprise Research Institute www.deri.ie foaf:Person myspace:topFriend mo:MusicalArtist foaf:Person foaf:Person URIs foaf:Document URIs myspace (via DBTune) 0 1 0 1 1 0 1 0 0 1 0 1 0 1 foaf:interest .... foaf:Document foaf:Document sioc:links_to user-item matrix FOAF vocabulary sioct:WikiArticle wikipedia (via SIOC exporter) integrate with transform apply collaborative SPARQL CONSTRUCT from RDF graph ltering algorithm on query to matrix user-item matrix  Use Linked Data to augment private data  this enables multi-source recommendations:  recommend new items through external background data  reduce sparsity by adding connections from ext. backg. data  provide recommendations for new users by using an external profile Benjamin.Heitmann slide 9 of 19 @deri.org
    21. 21. Evaluation of multi-source recommendation results Digital Enterprise Research Institute www.deri.ie  Smart Radio: first online streaming and rec. radio  small: 190 users and 330 musicians binary cosine similarity i1, i2: items e.g. musical artists  create links to MySpace artists via DBpedia  external background data: 11000 relevant recommendations users, 25000 new connections  collaborative filtering with binary cosine similarity  evaluation compared to Last.FM as “gold standard”  improve precision: 2% -> 14%  improve recall: 7% -> 33% Benjamin.Heitmann slide 10 of 19 @deri.org
    22. 22. Data sharing for recommendations Digital Enterprise Research Institute www.deri.ie  2nd paradigm shift: sharing user profile data between sites  Reverse perspective: ? users want to move between social networks! data  Primary user recommendations sharing concern: privacy  Surprise: really hot topic from this perspective Benjamin.Heitmann slide 11 of 19 @deri.org
    23. 23. Facebook approach to privacy-enabled user profiles Digital Enterprise Research Institute www.deri.ie  The Facebook approach: express preference  centralised user authentication for user action profile  data sharing for e.g. web site interaction recommendations cross domain  closed system data sharing if authorised  no portability at by user all!  Challenge: open alternative with portability and recommendations for privacy! external site provided by (at the same time) facebook Benjamin.Heitmann slide 12 of 19 @deri.org
    24. 24. Alternative: architecture for private and portable user profiles Digital Enterprise Research Institute www.deri.ie Benjamin.Heitmann slide 13 of 19 @deri.org
    25. 25. Alternative: architecture for private and portable user profiles Digital Enterprise Research Institute www.deri.ie  User profile: Benjamin.Heitmann slide 13 of 19 @deri.org
    26. 26. Alternative: architecture for private and portable user profiles Digital Enterprise Research Institute www.deri.ie  User profile:  Profile data expressed using RDF (FOAF+SIOC) FOAF Profile Benjamin.Heitmann slide 13 of 19 @deri.org
    27. 27. Alternative: architecture for private and portable user profiles Digital Enterprise Research Institute www.deri.ie  User profile:  Profile data expressed WebID using RDF (FOAF+SIOC)  WebID provides identity (2 parts) FOAF Profile Benjamin.Heitmann slide 13 of 19 @deri.org
    28. 28. Alternative: architecture for private and portable user profiles Digital Enterprise Research Institute www.deri.ie  User profile:  Profile data expressed WebID using RDF (FOAF+SIOC)  WebID provides identity private key (2 parts) – private SSL Key in user agent FOAF Profile Benjamin.Heitmann slide 13 of 19 @deri.org
    29. 29. Alternative: architecture for private and portable user profiles Digital Enterprise Research Institute www.deri.ie  User profile:  Profile data expressed WebID using RDF (FOAF+SIOC)  WebID provides identity private key public key (2 parts) – private SSL Key in user agent – public SSL Key in FOAF FOAF Profile profile Benjamin.Heitmann slide 13 of 19 @deri.org
    30. 30. Alternative: architecture for private and portable user profiles Digital Enterprise Research Institute www.deri.ie  User profile:  Profile data expressed WebID using RDF (FOAF+SIOC)  WebID provides identity private key public key (2 parts) – private SSL Key in user agent – public SSL Key in FOAF FOAF Profile profile  Roles: Benjamin.Heitmann slide 13 of 19 @deri.org
    31. 31. Alternative: architecture for private and portable user profiles Digital Enterprise Research Institute www.deri.ie  User profile:  Profile data expressed WebID using RDF (FOAF+SIOC)  WebID provides identity private key public key (2 parts) – private SSL Key in user agent – public SSL Key in FOAF user agent FOAF Profile profile  Roles:  user agents: manage user identities Benjamin.Heitmann slide 13 of 19 @deri.org
    32. 32. Alternative: architecture for private and portable user profiles Digital Enterprise Research Institute www.deri.ie  User profile:  Profile data expressed WebID using RDF (FOAF+SIOC)  WebID provides identity private key public key (2 parts) – private SSL Key in user agent – public SSL Key in FOAF user agent FOAF Profile profile  Roles: stored  user agents: manage user in identities  profile storage service: stores 1 or many profiles profile storage site Benjamin.Heitmann slide 13 of 19 @deri.org
    33. 33. Alternative: architecture for private and portable user profiles Digital Enterprise Research Institute www.deri.ie  User profile:  Profile data expressed WebID using RDF (FOAF+SIOC)  WebID provides identity private key public key (2 parts) – private SSL Key in user agent – public SSL Key in FOAF user agent FOAF Profile profile  Roles: stored  user agents: manage user in identities  profile storage service: retrieves user profile stores 1 or many profiles if user authorises it profile storage site data consumer  data consumers: provide services for users Benjamin.Heitmann slide 13 of 19 @deri.org
    34. 34. Communication pattern of the proposed architecture Digital Enterprise Research Institute www.deri.ie WebID private key public key Storage URI user agent FOAF Profile stored in profile storage site data consumer Benjamin.Heitmann slide 14 of 19 @deri.org
    35. 35. Communication pattern of the proposed architecture Digital Enterprise Research Institute www.deri.ie  Scenario: restaurant recommendation WebID  Assumption: user is logged into Openbook private key public key Storage URI user agent FOAF Profile stored in profile storage site data consumer Benjamin.Heitmann slide 14 of 19 @deri.org
    36. 36. Communication pattern of the proposed architecture Digital Enterprise Research Institute www.deri.ie  Scenario: restaurant recommendation WebID  Assumption: user is logged into Openbook private key public key 1. User requests nice restaurants Storage URI from Chow user agent FOAF Profile Any nice stored restaurants? in profile storage site data consumer Benjamin.Heitmann slide 14 of 19 @deri.org
    37. 37. Communication pattern of the proposed architecture Digital Enterprise Research Institute www.deri.ie  Scenario: restaurant recommendation WebID  Assumption: user is logged into Openbook private key public key 1. User requests nice restaurants Storage URI from Chow 2. Chow gets profile storage via user agent Firefox FOAF Profile Firefox stored provides in storage URI profile storage site data consumer Benjamin.Heitmann slide 14 of 19 @deri.org
    38. 38. Communication pattern of the proposed architecture Digital Enterprise Research Institute www.deri.ie  Scenario: restaurant recommendation WebID  Assumption: user is logged into Openbook private key public key 1. User requests nice restaurants Storage URI from Chow 2. Chow gets profile storage via user agent Firefox FOAF Profile 3. Chow redirects Firefox to stored Openbook for authorisation redirect to openbook in for authorisation profile storage site data consumer Benjamin.Heitmann slide 14 of 19 @deri.org
    39. 39. Communication pattern of the proposed architecture Digital Enterprise Research Institute www.deri.ie  Scenario: restaurant recommendation WebID  Assumption: user is logged into Openbook private key public key 1. User requests nice restaurants Storage URI from Chow 2. Chow gets profile storage via user agent Firefox FOAF Profile 3. Chow redirects Firefox to User authorises stored Openbook for authorisation Openbook to in show parts of 4. User authorises Openbook to profile to Chow show some profile parts to Chow profile storage site data consumer Benjamin.Heitmann slide 14 of 19 @deri.org
    40. 40. Communication pattern of the proposed architecture Digital Enterprise Research Institute www.deri.ie  Scenario: restaurant recommendation WebID  Assumption: user is logged into Openbook private key public key 1. User requests nice restaurants Storage URI from Chow 2. Chow gets profile storage via user agent Firefox FOAF Profile 3. Chow redirects Firefox to stored Openbook for authorisation in 4. User authorises Openbook to show some profile parts to Chow 5.Openbook redirects to Chow redirect back to Chow profile storage site data consumer Benjamin.Heitmann slide 14 of 19 @deri.org
    41. 41. Communication pattern of the proposed architecture Digital Enterprise Research Institute www.deri.ie  Scenario: restaurant recommendation WebID  Assumption: user is logged into Openbook private key public key 1. User requests nice restaurants Storage URI from Chow 2. Chow gets profile storage via user agent Firefox FOAF Profile 3. Chow redirects Firefox to stored Openbook for authorisation in 4. User authorises Openbook to Chow retrieves profile show some profile parts to Chow parts now 5.Openbook redirects to Chow profile storage site 6.Now Chow accesses parts of data consumer profile data on openbook Benjamin.Heitmann slide 14 of 19 @deri.org
    42. 42. Cross-domain recommendations Digital Enterprise Research Institute www.deri.ie Benjamin.Heitmann slide 15 of 19 @deri.org
    43. 43. Cross-domain recommendations Digital Enterprise Research Institute www.deri.ie  3rd paradigm shift: Provide relevant results beyond your domain  Requires user data from multiple domains Benjamin.Heitmann slide 15 of 19 @deri.org
    44. 44. Cross-domain recommendations Digital Enterprise Research Institute www.deri.ie  3rd paradigm shift: Provide relevant results beyond your domain  Requires user data from multiple domains  Hunch.com shows one solution: ask your users Benjamin.Heitmann slide 15 of 19 @deri.org
    45. 45. Cross-domain recommendations Digital Enterprise Research Institute www.deri.ie  3rd paradigm shift: Provide relevant results beyond your domain  Requires user data from multiple domains  Hunch.com shows one solution: ask your users Benjamin.Heitmann slide 15 of 19 @deri.org
    46. 46. Cross-domain recommendations Digital Enterprise Research Institute www.deri.ie  3rd paradigm shift: Provide relevant results beyond your domain  Requires user data from multiple domains  Hunch.com shows one solution: ask your users Benjamin.Heitmann slide 15 of 19 @deri.org
    47. 47. Cross-domain recommendations Digital Enterprise Research Institute www.deri.ie  3rd paradigm shift: Provide relevant results beyond your domain  Requires user data from multiple domains  Hunch.com shows one solution: ask your users Benjamin.Heitmann slide 15 of 19 @deri.org
    48. 48. Cross-domain recommendations Digital Enterprise Research Institute www.deri.ie  3rd paradigm shift: Provide relevant results beyond your domain  Requires user data from multiple domains  Hunch.com shows one solution: ask your users Benjamin.Heitmann slide 15 of 19 @deri.org
    49. 49. Cross-domain recommendations Digital Enterprise Research Institute www.deri.ie  3rd paradigm shift: Provide relevant results beyond your domain  Requires user data from multiple domains  Hunch.com shows one solution: ask your users Benjamin.Heitmann slide 15 of 19 @deri.org
    50. 50. Cross-domain recommendations Digital Enterprise Research Institute www.deri.ie  3rd paradigm shift: Provide relevant results beyond your domain  Requires user data from multiple domains  Hunch.com shows one solution: ask your users  Alternative solution: automatically link domains and communities Benjamin.Heitmann slide 15 of 19 @deri.org
    51. 51. Future work: using Linked Data for cross-domain recommendations Digital Enterprise Research Institute www.deri.ie  Exploit the intrinsic links between John Cage Johnny Cash Elvis Metallica sources: Myspace  links between data domain: music from different sources KyleButler TheTeacher  connections between FOAF possible different domains profile recommendations  identical users in Dexter Morgan different communities Wikipedia many domains Country: City: Sport:  Requires links between Netherlands Amsterdam Sailing data sources (a.k.a. “The Linkage Problem) Benjamin.Heitmann slide 16 of 19 @deri.org
    52. 52. Summary Digital Enterprise Research Institute www.deri.ie  Recommendations have become a commodity  they are required for a good user experience  3 coming paradigm shifts:  Go beyond the context of one site (multi-source recommendations)  Provide results beyond your primary domain (cross-domain recommendations)  Enable eco-systems built around portable user profiles (Privacy-enabled user profile portability)  Developing eco-systems with multiple parties requires an architecture (mutual agreement on roles, standards and communication patterns) Benjamin.Heitmann slide 17 of 19 @deri.org

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