SO IS IT CORRECT OR NO OOPS YOU NEVER KNOW WHETHER YOU'VE GOT THESE THINGS ON RIGHT OR NOT YOU NEVER KNOW WHETHER THEY'RE ON RIGHT OR NOT YEAH WELL SO WE HOPE OKAY SO I DON'T NEED THAT OKAY SO DAVID CAME IN TO ME AND TO SAY UH I SOMETHING WE'D BEEN TALKING ABOUT FOR A WHILE WAS SOME STUFF I'D BEEN WORKING ON ABOUT USING THESE TOPIC MODELS WITH UH WITH NETWORKS WITH N THINGS WITH LINKS BETWEEN THEM DOCUMENTS THAT ARE LINKED UH N UH IT'S ACTUALLY IT'S RELATED TO RAPPLE BUT IT'S NOT ACTUALLY THAT TH THIS IS SOMETHING I HAVEN'T ACTUALLY BEEN WORKING ON FOR A COUPLE OF MONTHS NOW UH JUST BECAUSE I CAME UP WITH SOMETHING MORE INTERESTING THAT I'VE BEEN WORKING ON BUT UH TH AT THE TIME BASICALLY I'D BEEN TRYING TO USE P. L. S. A. AND THOSE TYPES OF MODELS ON WEB PAGES AND THAT CAME UP WITH UH A BIG PROBLEM IN THAT MOST WEB PAGES HAVE VERY VERY LITTLE TEXT ON THEM MM-HMM BUT YOU CAN TELL WHAT THEY'RE ABOUT BASED ON THE OTHER TEXTS ON THE SITE THE TEXTS ON THE THINGS THEY'RE LINKED TO SO WHAT I WANTED TO KNOW WAS CAN YOU COMBINE THE TOPIC MODELS THAT THE PAGES THAT HAVE LOTS OF TEXT ON THEM WITH SOMETHING ELSE THAT THAT TELLS YOU WHAT A PAGE THAT HAS NO OR VERY LITTLE TEXT ON IT BASED ON THE LINKS YEAH SO QUITE SIMPLY IF YOU HAVE A PAGE ONE WHICH IS LIKE A HOME PAGE AND THAT'LL MAYBE HAVE FIVE LINKS AND A PICTURE ON IT AND THEN BECAUSE IT LINKS TO PAGE TWO AND PAGE THREE CAN YOU TELL WHAT THIS IS ABOUT MM-HMM YEAH SO I STARTED LOOKING AT UH GOOGLE PAGE RANK WHICH KINDA DOES THIS BY SAYING WELL THE LINKS GO THE OTHER WAY IT SAYS IF THIS PAGE IS ABOUT FISH OR IN GOOGLE THEY SAY IF THIS PAGE IS GOOD IN SOME WAY AND IT LINKS TO THIS OTHER PAGE AND WE DON'T KNOW WHAT THIS IS THEN THAT PAGE IS MORE LIKELY TO BE GOOD AND IT'S JUST PAGE RANK IS JUST A SIMPLE ALGORITHM FOR DOING THIS OVER THE WHOLE BIG NETWORK SO I WANTED TO COMBINE THE TOPIC MODELS THAT TELL YOU BEFOREHAND WHAT YOU THINK THIS PAGE AND THIS PAGE ARE ABOUT WITH THIS TO MAYBE GET A BETTER IDEA SO THE IDEA IS SAY YOU'VE GOT SOME TEXT ON A PAGE THAT'S ONLY PART OF THE PICTURE ABOUT WHAT OF WHAT THE PAGE IS REALLY ABOUT AND THE LINKS POINTING TO THAT PAGE ARE ALSO PART OF THE PICTURE SO I BASICALLY CAME UP WITH AN ALGORITHM THAT'S JUST A I IT PLUGS P. L. S. A. UH PLUGS PAGE RANK INTO P. L. S. A. RIGHT SO PAGE RANK YOU BASICALLY JUST D UH SAY YOU BASICALLY TAKE THE HOW GOOD THIS PAGE IS WHICH IS X. AND HOW GOOD THIS PAGE IS WHICH IS Y. AND YOU THEN SAY THIS PAGE IS BASED ON UH HOW DO YOU DO THIS BASED ON THE THE LINKS COMING INTO IT UH IT'S JUST A LIKE A SUM OVER ALL THE THE LINKS OF UH WELL X. LINK I GUESS O OF OF THE VALUE UH UH THE BUT IT'S NOT IT'S YEAH IF IF SO IF THIS IS PAGE RANK OF X. PAGE RANK OF Y. I THINK THIS IS ROUGHLY IT OFF HAND PAGE RANK OF Z. SO YOU SAY PAGE RANK OF Z. IS EQUAL TO UH ONE OVER A CONSTANT TO NORMALISE IT UH SUM OVER ALL THE LINKS INTO Z. OF PAGE RANK MM-HMM YEAH THA IT'S LIKE THAT YEAH SO SIMILAR IDEA IF YOU GOT A VECTOR THAT SAYS WHAT THE TOPICS ARE SAY YOU'VE LEARNED FROM P. L. S. A. A D A DISTRIBUTION OF TOPICS FOR EVERY PAGE SO IF YOU SAY A PROBABILITY OF TOPIC ONE FOR UH FOR PAGE UH Z. UH IS EQUAL TO AND YOU CAN USE THE SAME SORT OF THING RIGHT SO NICKING A WHOLE BUNCH OF MATHS FROM HOW YOU WORK OUT PAGE RANK UH IT COMES UP WITH UH JUST UH AN ITERATIVE ALGORITHM WHICH IS JUST UH THE CURRENT PROBABILITY OF TOPIC ONE GIVEN Z. SO THAT'S PROBABILITY OLD PLUS UH OKAY YEAH SO W BASICALLY WHAT YOU'RE SAYING HERE ONE MINUS ALPHA SAY LINK CONTRIBUTION YOU CAN CALL THIS I GUESS SO WHAT YOU'RE SAYING IS THAT A PAGE IS PARTLY FROM ITS TEXT THAT YOU'VE GOT ON THE PAGE AND IT'S PARTLY FROM WHAT THE LINKS SAY IT'S ABOUT SO THAT ALPHA THING IS HOW MUCH DO YOU TRUST THE TEXT ON THE PAGE AND HOW MUCH DO YOU TRUST THE LINKS SO THAT ALPHA COULD BE DIFFERENT FOR EVERY PAGE MM OKAY UH AND I HAD SOME RESULTS FROM THAT BUT THEY WERE ALL ON TEMP TWO SO THAT'S NOT IMPORTANT RIGHT NOW UH BUT AS IT IS NOT REALLY IMPORTANT I CAN REGENERATE THAT THE PROBLEM WAS THAT THIS GIVES YOU A NEW VECTOR OF TOPICS FOR EVERY PAGE SO YOU'VE GOT TWO VECTORS YOU'VE GOT THE ONE THAT YOU'VE JUST GOT FROM P. L. S. A. OR WHATEVER MODEL AND YOU'VE GOT THE ONE THAT YOU GET FROM THIS UH HOW DO YOU TELL WHICH ONE'S BETTER I HAD NO WAY TO EVALUATE THIS WELL HOW DO YOU TELL THAT THIS PAGE BECAUSE YOU DON'T KNOW WHERE THE TOPICS ARE 'CAUSE THEY'RE LEARNED AUTOMATICALLY UH SO YOU YOU HAVE TWO REPRESENTATION ONE WHICH COMES FROM THIS LINK UH STUFF AND ONE F THIS ONE IS NOT BASED ON P. L. S. A. TOO YEAH IT IS THE INITIALISATION OF Y A T. YEAH UH OKAY BUT THEN IT IS ITERATIVE YEAH OKAY OKAY OKAY SO YOU YOU SHOULD FIRST TRAIN THE P. L. S. A. STUFF THEN YOU INITIALISE THIS LINK UH SPREADING THING AND THEN YOU ITERATE OVER OKAY AND IT CONVERGE AND SO YOU HAVE THE BOOST UH INITIAL P. L. S. A. AND P. L. S. A. PLUS THE LINK THING THEN SO IT GIVES YOU A PROBABILITY OF A TOPIC GIVEN A DOCUMENT WHICH IS Z. GIVEN D. AND A PROBABILITY OF A WORD GIVEN A TOPIC SO THIS IS THE SAME FOR BOTH MODELS YEAH WELL YEAH WHAT YOU DO IS YOU'RE TAKING THIS AND ITERATING OVER IT THE PROBABILITY OF THE TOPIC GIVEN THE DOCUMENT UH SO HAVE I MADE THAT CLEAR I'M I'M NOT SURE IF I'M EXPLAINING THIS WELL OKAY RIGHT SO Y I YOU M YEAH SO WE WERE STRUGGLING AT HOW DO YOU EVALUATE THIS BECAUSE UH I MEAN IF YOU LOOK AT THE WAY THAT P. L. S. A. AND ALL THOSE KIND OF MODELS HAVE BEEN EVALUATED IT'S TH THEY NEVER QUANTITATIVE QUANTITATIVELY DO ANYTHING THEY ALWAYS SAY UH WELL LOOK AT THE CLUSTERING THERE'S HERE WE'VE GOT SOME DOCUMENTS THAT ALL APPEAR TO BE ABOUT THE SAME THING AND HERE'S WHERE WE GET SOME OTHER DOCUMENTS THAT ALL APPEAR TO BE ABOUT THE SAME THING