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Review Analysis WWW2012 Weinan Zhang 29 Feb. 2012
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General Info Acceptance Rate: 12% (108/885) Monetization Track – Gui-Rong Xue – About 60 submissions – 4~5 accepted papers
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Two papers Paper 301: Joint Optimization of Bid and Budget Allocation in Sponsored Search – Internet Advertising Team, MSRA Paper 324: A Semantic Approach to Recommending Text Advertisements for Images – ApexLab
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Paper 301 Joint Optimization of Bid and Budget Allocation in Sponsored Search – Sponsored Search Advertiser-Oriented Service
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Solution Probabilistic Model for Ad Ranking Joint Optimization on Bid Price and Campaign Budget Experiment on Simulator
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Review Comments RatingConfidence Borderline (0)Medium (2) Borderline (0)High (3) Weak accept (1)High (3)
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Pros Interesting and important problem Real auction data Good written
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Cons Budget constraint The optimization problem and solution are straightforward The experiment is only a simulation
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Sum up of paper 301 Three times – SIGIR, WSDM, WWW – More than 10 footnotes now Unsolved points – Straightforward model – Simulation – Value per click estimation Submit to KDD
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Paper 324 A Semantic Approach to Recommending Text Advertisements for Images – Cross-media Mining – Thesis of bachelor – First submission
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Visual Contextual Advertising
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Our Solution JeepCar Auto Vehicle Plane Truck
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Review Comments RatingConfidence Weak Reject (-1) High (3) Weak Reject (-1)Expert (4) Weak Accept (1)High (3)
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Pros Semantic match outperforms syntactic matching Interesting – “The idea is very interesting and I would love to see this as a full paper ” but…
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Cons Image and ads may not match any concept – Even Wikipedia is not sufficient Part of ads collection is retrieved by WordNet words Matching between knowledge bases is trivial in this paper Should provide more detailed results – Accuracy of each node of ImageNet
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Sum up of paper 324 Adding knowledge bases – Wikipedia – More LOD here – Folksonomy Not just knowledge bases – Image: Image annotation, ViCAD – Text Ads: Bid Keywords Deeper experiment results Plan to WSDM
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Lessons Learned More detailed experimental results – Accuracy of locating nodes in Imagenet for input images – Effectiveness of different matching functions More non-experiment efforts – Discussion – Writing
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Thank you
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