Notes from From Understanding to Enabling Networks: Using Web Science to Enhance Recommender Systems

Noshir Contractor

Noshir Contractor

The keynote at #recsys2011 is by Noshir Contractor. He is the coauthor of “Theories of communication networks” which seems to be an interesting book from amazon reviews.

the presentation stack is available here (thanks to @barrysmyth for the link)

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?

MTML model:

  • 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
My note: How about Robert Spolsky’s theory?
Exponential randome graphs can explain how these collaboration networks is formed (the shape of the graph)
They have looked at the structure of NSF proposals and they wanted to see if they can build a recommnder system that by using characteristics of the proposal make recommendations for acceptance
The likelihood of collaboration is highers if:
  • you have written an NSF proposal together
  • you have cited each other
Didn’t know about H-index. Interesting factor. Apparently those with higher H-index are less likely to collaborate.
Citing your collaborators actually reduces the likelihood of getting NSF funding (!)
Solving the link recommendation problem (recommending who should be on the team)
Link prediction approaches: node-wise similarity, network topology, or probabilistic modeling
P* for link prediction
Use p* models to calculate link probability
  • Estimate p*/ERGM
  • the rest I didn’t get to type (!)
I think the probabilistic model that he is refering to is the same as model fitting on Bayesian nets but I am not sure.
The talk ended by a demo of the implementation that is available here 
Noshir’s book is also available for free on his personal website



~ by marksalen on October 24, 2011.

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