Review AnalysisWWW2012

Weinan Zhang29 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

Rating ConfidenceBorderline (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

Rating ConfidenceWeak 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