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					Sponsored Search


  Cory Pender
 Sherwin Doroudi
  Optimal Delivery of Sponsored Search
Advertisements Subject to Budget Constraints



              Zoe Abrams
            Ofer Mendelevitch
             John A. Tomlin
Introduction

 Search engines (Google, Yahoo!, MSN)
  auction off advertisement slots on
  search page related to user’s keywords
 Pay per click
 Earn millions a day through these
  auctions
    – Auction type is important
    Sponsored search parameters

 Bids
 Query frequencies
                        QuickTime™ and a
    – Not controlled by advertisers or search engine
                    TIFF (LZW) decompressor
                               decompressor
                 are needed to see this picture.


    – Few queries w/ large volume, many with low
      volume
 Advertiser budgets
 Pricing and ranking algorithm
Solution

 Focus on small subset of queries
  – Predictable volumes in near future
  – Constitute large amount of total volume
 Sponsored search parameters
 Bids
 Query frequencies
 Advertiser budgets
    – Controlled by advertisers
   Pricing and ranking algorithm
    – Generalized second price (GSP) auction
    – Rankings according to (bid) x (quality score)
    – Charged minimum price needed to maintain rank
   Goal: take these parameters into account,
    maximize revenue
Motivating example
Reserve price is 
Bidder      Bid for q1 Bid for q2 Budget
b1          C1 +     C1         C1
b2          C1        0          C1
b3          C1 -     C1 -      2 C1

Allocation Shown       Shown     Total
           for q1      for q2    Revenue
Greedy     b1          b3        C1 + 
Optimal      b2        b1        2C1 - 
 Problem Definition
 Queries Q = {q1, q2, q3, ..., qN}
 Bidders B = {b1, b2, b3, ..., bM}
 Bidding state A(t); Aij(t) is j’s bid
     for i-th query
 dj is j’s daily budget
 vi is estimate of query frequency
 Li = {jp : jp  B, p = 1, ..., Pi}
 Lik = {jik : jik  Li, l ≤ Lik ≤ P}
    Ranking and revenue
                                 QuickTime™ and a
   Bid-ranking -            TIFF (LZW) decompressor
                          are needed to see this picture. and a
                                             QuickTime™
   Revenue-ranking -                       TIFF (LZW) decompressor
                                             needed to see this
                                       areQuickTime™ and a picture.
                                      TIFF (LZW) decompressor
   So, for slate k,               are needed to see this picture.
                            QuickTime™ and a
   Price per click: are needed to see this picture.
                        TIFF (LZW) decompressor


   Independent click through rates                                                    Q uic kT im e™ an d a
                                                                                  T IFF ( LZ W) d ec o mp r es s or
                                                                               a re n ee d ed to s e e th is p ictu r e.




                                                                      QuickTime™ an d a
   Revenue-per-search:                                           TIFF (LZW) decompressor
                                                               are need ed to see this p icture .




Total revenue:
                                 Qui ckTi me™ and a
                            TIFF (LZW) decompresso r
                         are ne ede d to see thi s pi cture.
Bidder’s cost
                                         Quic kTime™ and a

   Total spend for j:               TIFF (LZW) dec ompressor
                                  are needed to s ee this pic ture.




                       QuickTime™ and a
                   TIFF (LZW) decompressor
                are neede d to see this picture.
Linear program
 Queries i = 1, ..., N
 Bidders j = 1, ..., M
 Slates k = 1, ..., Ki
 Data: dj, vi, cijk, rik
 Variables: xik
 Constraints:
                           QuickTime™ and a
    – Budget:          TIFF (LZW) decompressor
                    are needed to see this picture.


                          QuickTime™ and a
    – Inventory:      TIFF (LZW) decompress or
                   are needed to see this picture.
Objective function
                                   Quic kTime™ and a

   Maximize revenue:          TIFF (LZW) dec ompress or
                            are needed to s ee this picture.




                                  QuickTime™ and a
                              TIFF (LZW) decompressor
   Value objective:       are needed to see this picture.


                               QuickTime™ and a
                           TIFF (LZW) decompressor
   Clicks objective:   are needed to see this picture.
Column Generation
 Each column represents a slate
 Could make all possible columns
    – But for each query, exponential in number
      of bidders
 Start with some initial set of columns
 j: Marginal value for j’s budget
 i: Marginal value for ith keyword
                   QuickTime™ and a
 Profit if areTIFF (LZW) see this picture.
                needed to
                          decompressor

                     QuickTime™ and a

 Maximize
                 TIFF (LZW) decompressor
              are needed to see this picture.
 How to maximize?
  If small number of bidders for a query,
   enumerate all legal subsets Lik, find
   maxima, see if adding increases profit
  Otherwise, use algorithm described in
   another paper
                          QuickTime™ and a
                     TIFF (LZW) decompressor
                  are needed to see this picture.
 ebay.com                 Qui ckTi me™ and a
                                                           nextag.com
                      TIFF (LZW) decomp re ssor
                   are ne ede d to see thi s pi cture.




tigerdirect.com
                                  ?                      priceline.com
Summary (so far)

   Various bidders vying for spots on the slate
    for each query
   Constrained by budget, query frequencies,
    ranking method
   Solve LP for some initial set of slates
   Check if profit can be made by adding new
    slates
   Re-solve LP, if necessary
   Can be applied to maximize revenue or
    efficiency
Simulation Methodology
   Compare this method to greedy algorithm
    – For greedy, assign what gets most revenue at the
      time; when bidder’s budget is reached, take them
      out of the pool
 Used 5000 queries
 For 11 days, retrieved hourly data on bidders,
  bids, budgets
 To determine which ads appear, assign
  based on frequencies fik = xik/vi
 After each hour, see if anyone has exceeded
  budget
Simulation Results

 Current method better than greedy
  method, when optimizing over revenue
  or efficiency
 Larger gain for revenue when revenue
  optimized
 Revenue and efficiency are closely tied
Gains when efficiency is maximized



                  QuickTime™ an d a
              TIFF (LZW) decomp resso r
           are need ed to see this picture .
Gains when revenue is maximized



                 QuickTime™ an d a
             TIFF (LZW) decomp ressor
          are need ed to see this p icture .
Impact on bidders



               QuickTime™ and a
           TIFF (LZW) decompressor
        are neede d to see this picture.
Limitations

 Illegitimate price hikes for other bidders
  if one person exceeds budget in middle
  of hour
 Assumption that expected number of
  clicks are correct
 For the purposes of the simulation,
  expect these to affect greedy and LP
  optimization similarly
Future work
   Focus on less frequent queries
    – Frequencies harder to predict
    – Some work has been done (doesn’t incorporate
      pricing and ranking)
 Keywords with completely unknown
  frequencies
 Parallel processing for submarkets
 Investigate how advertisers might respond to
  this method
    – Potential changes in reported bids/budgets

				
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posted:10/13/2012
language:English
pages:22