1. Optimal Ad Ranking for Profit Maximization Raju Balakrishnan (Arizona State University) Subbarao Kambhampati (Arizona State University) TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A

2. WebDB ‘08 Optimal Ad Ranking for Profit Maximization Ad Ranking: State of the Art Sort by Bid Amount x Relevance We Consider Ads as a Set, and ranking is based on User’s Browsing Model Sort by Bid Amount Ads are Considered in Isolation, Ignoring Mutual influences.

3. WebDB ‘08 Mutual Influences Optimal Ad Ranking for Profit Maximization Three Manifestations of Mutual Influences on an Ad are 1.Similar ads placed above  Reduces user’s residual relevance of the ad 2.Relevance of other ads placed above  User may click on above ads may not view the ad 3.Abandonment probability of other ads placed above  User may abandon search and not view the ad

4. WebDB ‘08 User’s Browsing Model Optimal Ad Ranking for Profit Maximization User Browses Down Staring at First Ad  Abandon Browsing with Probability  Goes Down to next Ad with probability At every Ad he May Process Repeats for the Ads Below With a Reduced Probability  Click the Ad With Relevance Probability If is similar to residual relevance of goes down and abandonment probabilities goes up.

5. WebDB ‘08 Optimal Ad Ranking for Profit Maximization Expected Profit Considering Ad Similarities Considering Bid Amounts ( ), Residual Relevance ( ), abandonment probability ( ), and similarities the expected profit from a set of n ads is, THEOREM: Optimal Ad Placement Considering Similarities between the ads is NP-Hard Proof is a reduction of independent set problem to choosing top k ads considering similarities. (Refer Paper for Proof) Expected Profit =

6. WebDB ‘08 Dropping similarity, hence replacing Residual Relevance ( ) by Absolute Relevance ( ), Ranking to Maximize This Expected Profit is a Sorting Problem Optimal Ad Ranking for Profit Maximization Expected Profit Considering other two Mutual Influences (2 and 3) Expected Profit =

7. WebDB ‘08 Optimal Ad Ranking for Profit Maximization Optimal Ranking  The physical meaning RF is the profit generated for unit consumed view probability of ad  Ads above have more view probability. Placing ads producing more profit per consumed view probability is intuitively justifiable. (Refer paper for proof of optimality) Rank ads in Descending order of:

8. WebDB ‘08 Comparison to Yahoo and Google Yahoo!  Assume abandonment probability is zero Google Assume where is a constant for all ads Optimal Ad Ranking for Profit Maximization Assumes that the user has infinite patience to go down the results until he finds the ad he wants. Assumes that abandonment probability is negatively proportional to relevance.

9. WebDB ‘08 Optimal Ad Ranking for Profit Maximization Quantifying Expected Profit Proposed strategy gives maximum profit for the entire range Bid Amount Only strategy becomes optimal at 45.7% 35.9% Number of Clicks Zipf Random with exponent 1.5 Abandonment Probability Uniform Random as Relevance Uniform Random as Bid Amounts Uniform Random Difference in profit between RF and competing strategy is significant

10. WebDB ‘08 Optimal Ad Ranking for Profit Maximization Contributions  Extending Expected Profit Model of Ads Based on Browsing Model, Considering Mutual Influences  NP-Hardness proof for placement considering similarities.  Optimal Ad Ranking Considering Mutual Influences Other than Ad Similarities.  Subsumes Google and Yahoo placement as special cases  Simulation shows significant improvement in expected profit.  Hope to evaluate by assessing abandonment probabilities ( future work )