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Ideation 101
Mobile, Social, Commerce,
Media, Science, Other
Hack Categories
Some problem challenges
from our side
Geo
• Increasing place coverage
• Increasing language coverage
• Places provision though
crowdsourcing
Detecting Peer to Peer Insults
• Comments many times goes personal and
insulting in debates or discussion. For eg :-
discussion on any comment made by some
political leader.
• We want to identify comments that is intended to
insult other participant in discussion.
• We don’t want to profane on comments made on
topic in discussion
Recommendation Challenges in WebMedia
• Traditional Content based recommendation and
Collaborative filtering has higher rate of false
positives.
• Recommendation based on social signals is also
not efficient and there is high probability
different likings between friends.
• Cold Start Problem:- How to recommend user
who is new to system
Comment Text Relevance
• How do we find comments/posts that are
relevant to the article?
• Can we rank all comments wrt relevance
• If not, can we atleast find the least relevant
comments, and use that signal to bump down a
comment that is otherwise of good quality?
• Can we find highly relevant comments, even if
they are very short?
Flickr Challenges
• In a photo stream how to choose what next
photo should be shown user to keep him
engaged.
• Creating implicit following graph in flickr based
on clicks on a user photo by someone
Thank You!

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Ideation101 - Hacku ideas

  • 2. Mobile, Social, Commerce, Media, Science, Other Hack Categories
  • 4. Geo • Increasing place coverage • Increasing language coverage • Places provision though crowdsourcing
  • 5. Detecting Peer to Peer Insults • Comments many times goes personal and insulting in debates or discussion. For eg :- discussion on any comment made by some political leader. • We want to identify comments that is intended to insult other participant in discussion. • We don’t want to profane on comments made on topic in discussion
  • 6. Recommendation Challenges in WebMedia • Traditional Content based recommendation and Collaborative filtering has higher rate of false positives. • Recommendation based on social signals is also not efficient and there is high probability different likings between friends. • Cold Start Problem:- How to recommend user who is new to system
  • 7. Comment Text Relevance • How do we find comments/posts that are relevant to the article? • Can we rank all comments wrt relevance • If not, can we atleast find the least relevant comments, and use that signal to bump down a comment that is otherwise of good quality? • Can we find highly relevant comments, even if they are very short?
  • 8. Flickr Challenges • In a photo stream how to choose what next photo should be shown user to keep him engaged. • Creating implicit following graph in flickr based on clicks on a user photo by someone

Editor's Notes

  • #2: People in my team put together the Movieplex. Who else gets to watch movies as a part of their job? 