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Trust and Profit Sensitive Ranking for On-line Ads and Web Databases

Trust and Profit Sensitive Ranking for On-line Ads and Web Databases Raju Balakrishnan, advised by Subbarao Kambhampati rajub@asu.edu rao@asu.edu. Three Manifestations of Mutual Influences on an ad are Similar ads placed above Reduces user’s residual relevance of the ad

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Trust and Profit Sensitive Ranking for On-line Ads and Web Databases

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  1. Trust and Profit Sensitive Ranking for On-line Ads and Web Databases Raju Balakrishnan, advised by Subbarao Kambhampati rajub@asu.edu rao@asu.edu • Three Manifestations of Mutual Influences on an ad are • Similar ads placed above • Reduces user’s residual relevance of the ad • Relevance of other ads placed above • User may click on ads above and may not view the ad • Abandonment probability of other ads placed above • User may abandon search and not view the ad Optimal Ad-Ranking Considering Mutual-Influences Comparison of Placement Strategies Ad Placement Strategies Sort by Bid x Relevance Sort by Bid Ads are Considered in Isolation, Ignoring Mutual influences. We Consider Ads as a Set, and ranking is based on User’s Browsing Model Expected Profit Optimal Ranking 35.9% 45.7% THEOREM: Optimal Ad Placement Considering Similarities between the ads is NP-Hard SourceRank: Trust and Relevance based Ranking of Web Databases for the Deep Web Evaluated in TEL-8 movies and books web databases ( 22 each). Millions of Databases: Which are Trustworthy and Relevant? Agreement Graph Agreement Implies Trust & Relevance! Top-5 Precision-Movies SourceRank is calculated as the stationary visit probability of a weighted random walk on the database vertex in agreement graph. Coverage of a Source is the sum of the relevances of the tuples Trustworthiness is evaluated as the decrease in ranks of corrupted sources. Combine Coverage and SourceRank http://rakaposhi.eas.asu.edu/scuba

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