The  2015  IEEE/ACM  International  Conference  on  Advances  in  Social  Networks  Analysis  and  Mining  

 (ASONAM  2015)    

Paris,  France,  August  25-­‐‑28,  2015   � 

Debrief by Tao Chen Sep 25, 2015

Paris,  France � 

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Eiffel  Tower

Image  Credits:  hMp://y0.ifengimg.com

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Louvre  Museum

Image  Credits:

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Winged  Victory  of  Samothrace Venus

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Notre  Dame  de  Paris  (巴黎圣母院)

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Bell  Tower � 

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On  the  top  of  bell  tower � 

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Musée  de  l'ʹOrangerie

13 Image  Credits:  hMps://plumasdepaloma.files.wordpress.com

Image  Credits:  hMps://en.wikipedia.org/wiki/Musée_de_l%27Orangerie

Back  to  ASONAM   � 

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Big  Picture  of  ASONAM � •  Submission statistics

o  272 valid submission from 52 countries o  Full paper: 49 (18.0%) o  Short paper: 47 (17.3%) o  Poster paper: 30 (11.0%) o  Singapore has 100% acceptance rate!

•  Program o  One day: workshops,tutorials and posters o  Three days:

•  5 Keynotes •  21 sessions (3 in parallel and 30 minutes each)

•  Venue: Telecom-ParisTech

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16 Image  Credits:  hMps://twiMer.com/jcasasr/status/636471113300750336/photo/1

Keynote  1:  Sinan  Aral,  MIT The  Dynamics  of  Social  Influence  and  Reputation  Online    

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Big  Picture  of  ASONAM � •  Submission statistics

o  272 valid submission from 52 countries o  Full paper: 49 (18.0%) o  Short paper: 47 (17.3%) o  Poster paper: 30 (11.0%) o  Singapore has 100% acceptance rate!

•  Program o  One day: workshops,tutorials and posters o  Three days:

•  Keynotes •  21 sessions (3 in parallel and 30 minutes each)

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Banghui  was  in  presentation � 

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First  time  to  be  a  session  chair!

What  impressed  me  most? � 

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Interesting  Papers � 

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Reciprocal  Recommendation  System  for  Online  Dating � 

•  Peng Xia et al. University of Massachusetts Lowell

•  Match users who are most likely to communicate with each other

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Reciprocal  Recommendation  System  for  Online  Dating � 

•  Memory based Collaborative filtering algorithm

•  Define two neighbor sets o  Sent (x)= { all users that x has sent msg to} (out) o  Receive (x)= { all users that x has received msg from} (in)

•  Intuition: Given a target user x, if a candidate y is similar to the Sent(x) and Receive(x), it is likely x and y will communicate

•  For users in the same gender o  Interesting similarity: send msg to same users o  Attractiveness similarity: receive msg from same users

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Reciprocal  Recommendation  System  for  Online  Dating � 

•  Dataset from Baihe.com (a Chinese site) o  200K sampled users (69.7% male, and 30.3% female) o  8 weeks of online dating interaction records o  Not able to public the dataset

•  Evaluate on two tasks o  X will send the first msg to Y o  X and Y have conversation (i.e., Y will also reply to X)

•  Users are active in a relative short period

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