Notes from From Understanding to Enabling Networks: Using Web Science to Enhance Recommender Systems
He started by presenting SNIF. SNIF is a device and social networks for dogs! Kind of social petworking. In contrast lovegety is the SNIF technology for people. Find love through random encounters.
Today we will talk about How we can take research in social sciences and bring it to recommender systems.
People have looked at citations and papers and found that people who write papers in teams have a high impact. Also articles by teams from different disciplines from different geographic locations have the highest impact. Fining the appropriate team from a diverse background and geography is much harder.
Thus we are looking at assembeling these type of teams. But how do we decide whom to bring to the team?
The exciting thing about our time is that we have theories, data and methods, additionally we have computation infrastructure to run these models
Why do people collaborate with each other?
- self interest (from econ theories)
- Social and resource exchange
- Mutual interest and collective action
- Theories of contagion
- Theories of balance
- Theories of homophily
- Theories of proximity
- you have written an NSF proposal together
- you have cited each other
- Estimate p*/ERGM
- the rest I didn’t get to type (!)