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[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Activities using Social Network Data
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[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Activities using Social Network Data

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The aim of the Experience Discovery project is to recommend extracurricular activities to high school and middle school students in urban areas. In implementing this system, we have been able to make …

The aim of the Experience Discovery project is to recommend extracurricular activities to high school and middle school students in urban areas. In implementing this system, we have been able to make use of both usage data and data drawn from a social networking site. Using pilot data, we are able to show that very simple aggregation techniques applied to the social network can improve recommendation accuracy.

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  • 1. Experience Discovery: Hybrid Recommendationof Student Activities using Social Network Data Robin Burke, Yong Zheng, Scott Riley Web Intelligence Laboratory College of Computing and Digital Media DePaul University
  • 2. Problem Service organizations offer many educational programs and activities for youth Participation (especially by underprivileged youth) is low  Even though these are the individuals who would benefit the most How to get better participation?  not just a recommendation problem
  • 3. The Role ofRecommendation Need for personalization  Many diverse activities  from basketball to poetry to robots to knitting  Low tolerance for imprecise results Need for system initiative  user research shows that students are unlikely to search and browse  To “pull” opportunities  system should “push” suggestions  we are considering mobile platforms
  • 4. Partners Digital Youth Network  service organization focused on the creation of digital media  Nichole Pinkard YouMedia  school-based online social network  affiliated with DYN Chicago Learning Network  consortium of museums and non-profits Chicago Public Schools Funders  MacArthur Foundation  Gates Foundation
  • 5. Experience Discovery:Research opportunities Full cycle observation  activity enrollments  activity attendance  click-through  post-activity rating, tagging, reviewing Social network data  uploading of digital media  browsing / commenting behavior  friend / follower connections
  • 6. Research question 1 There are multiple important knowledge sources  past enrollment history  content data  social network data  log data Mixed vs integrated hybrid recommendation  should different knowledge sources be integrated in making recommendations?  or should recommendations of different types be presented side-by-side?
  • 7. Research question 2 Activities sometimes have a logical planned sequence  Video editing I -> Video editing II Sometimes they are sequenced idiosyncratically  Digital photography -> Zoo explorer I Educational goal  increase both depth and breadth of student participation The role of “curricula”  how can recommendations be used to increase both breadth and depth of student involvement?  what is the role of top-down vs bottom-up sequences in recommendation?
  • 8. Research question 3 Dynamics of interest  students mature a lot between 11 and 18  old activities may lose their appeal Dynamics of offerings  activities change from year to year and season to season  may not be explicit Coping with change  how can we ensure that recommendations don’t lag student interest?  how to detect and respond to program changes?
  • 9. Research question 4 Students aren’t the only ones with questions Service providers can get value, too  what activities should I offer and where?  how do my offerings compare to other groups?  what needs are not being met? Analytics and recommendations for service providers  what can we provide that is helpful and comprehensible?
  • 10. Architecture ! "#$%$( 0$,- $01 $( 2*31( ,4+ 1% , & *5 D#$% *31( *+ , ?+$( ), &6037& *)*, )8,9 6+ & ,E& *% A1%)@ F% 8, > )$%*0$, ( C 6##+0*31( , &6G$( 2*( 0$, 9 *)*, - $01 $( 2*31( , > #/ ), ( - $. / + ), ! ( A& $, ( ?*0@ $, ! "#$% $( )*+ & , ?*0@ $, : 10& , *+ ?1( BA/ %*31( . , ; $)< 1% =, 9 *)*, ! 7*+ *31( , / ! "#$% $( )*+ & , > )$%*0$, ( C - $. / + , ).
  • 11. Initial experiments Data (2 schools)  226 students  32 activities  3800 records  (now adding ~2000 enrollments and ~50 activities / month) Algorithms  collaborative / binary  collaborative / pseudo rating  content-collaborative meta-level hybrid  plus behavioral descriptors
  • 12. Pseudo-ratings Some activities are attended multiple times  evidence of strong interest Example  book discussion group Normalize to user’s profile  weight for activity a = # of times attending a / total attendances Can we normalize in other ways?  take into account how often something was offered
  • 13. Meta-level hybrid Use course topic descriptors  13 choices  health, music, visual arts, etc.  activities may include several topics Build a topic profile by summing over descriptions of all activities Compare users based on topic profiles  rather than attendance data
  • 14. Adding social network data Extracted 10 features from the social network  counts of uploaded media types  overall level of activity Used feature combination  content profile 8$93& .(: 4) $&0 , .  behavior profile ! , (, . 5 "(6 ) %7. ! "#$% () % & #. 1"2, 3& % , (, . ) .- ! "#$% () % #"- . & *+, 1"2, 3& % #"- . ) *+, %/0 ) ". %/0 ) ".
  • 15. Results Temporal leave-one-out evaluation  see Burke, 2010 Look at a user’s experience over time  looking at users divided by  # of enrollments (profile size)  profile diversity (# of different enrollments) Need to do more research  Hybrid 2 works best for large, diverse users  Doesn’t matter what you do for non- diverse users
  • 16. Conclusions We are in the early stages here Eager to get our hands on bigger data Many research questions Would like to hear ideas
  • 17. Thanks Questions / Comments / Ideas