These slide(s) are the result of our discussion session on social recommenders, adaptivity, and feedback at the RSSE'12.

1. Social Recommenders,
Adaptivity, and Feedback
Summary of the Breakout Session from RSSE’12
OhadBarzilay, Lars Heinemann, Dennis Pagano, Chris
Parnin, Luca Ponzanelli, Tobias Roehm, Igor
Steinmacher, Margaret-Anne Storey, Giuseppe Valetto,
AlexeyZagalsky
(in alphabetical order)

2. Social Recommenders, Adaptivity, and Feedback
1. Feedback matters.
a) Feedback to the community (to increase motivation)
b) Feedback to the system (to increase recommendation quality)
c) Both influence each other indirectly.
d) Adaptive RSSEs: What gets better – the data or the system?
2. People matter.
a) Social recommenders build on people and are to be used by people
b) When you recommend expert, they also need to have time to spare. It's easier to ask
bottom-up in hierarchical organizations
3. Trust matters.
a) As soon as your data gets used, it depends on where it is used if it is ok for you.
b) Trust also applies to whom to believe when recommendations are made (human vs.
system)
4. Usage Data matters.
a) To give relevant recommendations
5. New applications:
a) Collaborative filtering for features, applications
b) Mixing recommendation sources (e.g. stackoverflow, task descriptions)
c) Recommenders for which usage data to collect (meta-application)
d) Exploit Quick Fix