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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. Ad Ranking: State of the Art. Sort by Bid Amount x Relevance.

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Optimal ad ranking for profit maximization

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


Ad ranking state of the art
Ad Ranking: State of the Art

Sort by

Bid Amount x Relevance

Sort by

Bid Amount

Ads are Considered in Isolation, Ignoring Mutual influences.

We Consider Ads as a Set, and ranking is based on User’s Browsing Model

Optimal Ad Ranking for Profit Maximization


Mutual influences
Mutual Influences

  • 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 above ads may not view the ad

    • Abandonment probability of other ads placed above

      • User may abandon search and not view the ad

Optimal Ad Ranking for Profit Maximization


User s browsing model

If is similar to residual relevance of goes down and abandonment probabilities goes up.

User’s Browsing Model

  • User Browses Down Staring at First Ad

  • At every Ad he May

  • Click the Ad With Relevance Probability

  • Goes Down to next Ad with probability

  • Abandon Browsing with Probability

Process Repeats for the Ads Below With a Reduced Probability

Optimal Ad Ranking for Profit Maximization


Expected profit considering ad similarities
Expected Profit Considering Ad Similarities

Considering Bid Amounts ( ), Residual Relevance ( ), abandonment probability ( ), and similarities the expected profit from a set of n ads is,

Expected Profit =

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)

Optimal Ad Ranking for Profit Maximization


Expected profit considering other two mutual influences 2 and 3
Expected Profit Considering other two Mutual Influences (2 and 3)

Dropping similarity, hence replacing Residual Relevance

( ) by Absolute Relevance ( ),

Ranking to Maximize This Expected Profit is a Sorting Problem

Expected Profit =

Optimal Ad Ranking for Profit Maximization


Optimal ranking
Optimal Ranking

Rank ads in Descending order of:

  • 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)

Optimal Ad Ranking for Profit Maximization


Comparison to yahoo and google
Comparison to Yahoo and Google

Yahoo!

  • Assume abandonment probability is zero

Google

Assume

where is a constant for all ads

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.

Optimal Ad Ranking for Profit Maximization


Quantifying expected profit
Quantifying Expected Profit

Abandonment Probability

Uniform Random as

Bid Amount Only strategy becomes optimal at

Relevance

Uniform Random as

Difference in profit between RF and competing strategy is significant

Number of Clicks

Zipf Random with exponent 1.5

35.9%

Proposed strategy gives maximum profit for the entire range

45.7%

Bid Amounts

Uniform Random

Optimal Ad Ranking for Profit Maximization


Contributions
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)

Optimal Ad Ranking for Profit Maximization


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