Millions of Databases: Which are Trustworthy and Relevant? Trust and Profit Sensitive Ranking for On-line Ads and Web Databases Raju Balakrishnan, advised by Subbarao Kambhampati rajub@asu.edu rao@asu.edu 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 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 Expected Profit Optimal Ranking 45.7% 35.9% 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 Millions of Databases: Which are Trustworthy and Relevant? Evaluated in TEL-8 movies and books web databases ( 22 each). 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