Review Analysis WWW2012 Weinan Zhang 29 Feb. 2012

General Info Acceptance Rate: 12% (108/885) Monetization Track – Gui-Rong Xue – About 60 submissions – 4~5 accepted papers

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

Paper 301 Joint Optimization of Bid and Budget Allocation in Sponsored Search – Sponsored Search Advertiser-Oriented Service

Solution Probabilistic Model for Ad Ranking Joint Optimization on Bid Price and Campaign Budget Experiment on Simulator

Review Comments RatingConfidence Borderline (0)Medium (2) Borderline (0)High (3) Weak accept (1)High (3)

Pros Interesting and important problem Real auction data Good written

Cons Budget constraint The optimization problem and solution are straightforward The experiment is only a simulation

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

Paper 324 A Semantic Approach to Recommending Text Advertisements for Images – Cross-media Mining – Thesis of bachelor – First submission

Visual Contextual Advertising

Our Solution JeepCar Auto Vehicle Plane Truck

Review Comments RatingConfidence Weak Reject (-1) High (3) Weak Reject (-1)Expert (4) Weak Accept (1)High (3)

Pros Semantic match outperforms syntactic matching Interesting – “The idea is very interesting and I would love to see this as a full paper ” but…

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

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

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

Thank you