Embed Size (px)
DESCRIPTION
Search Advertising. These slides are modified from those by Anand Rajaram & Jeff Ullman. History of web advertising. Banner ads (1995-2001) Initial form of web advertising Popular websites charged X$ for every 1000 “impressions” of ad Called “CPM” rate Modeled similar to TV, magazine ads - PowerPoint PPT Presentation
Citation preview
Search Advertising
These slides are modified from those by Anand Rajaram & Jeff Ullman
History of web advertising Banner ads (1995-2001)
Initial form of web advertising Popular websites charged X$ for every
1000 “impressions” of ad Called “CPM” rate Modeled similar to TV, magazine ads
Untargeted to demographically tageted Low clickthrough rates
low ROI for advertisers
Performance-based advertising Introduced by Overture around 2000
Advertisers “bid” on search keywords When someone searches for that
keyword, the highest bidder’s ad is shown
Advertiser is charged only if the ad is clicked on
Similar model adopted by Google with some changes around 2002 Called “Adwords”
Ads vs. search results
Web 2.0 Performance-based advertising
works! Multi-billion-dollar industry
Interesting problems Search Engine: What ads to show for a
search? Advertiser: Which search terms should I
bid on and how much to bid? Will I be charged the full amount I bid?
User: Am I getting relevant ads? Should I click on them?
Simple Adwords problem A stream of queries arrives at the
search engine q1, q2,…
Several advertisers bid on each query When query qi arrives, search engine
must pick an ad to show Goal: maximize search engine’s
revenues Clearly we need an online algorithm!
Greedy algorithm Select the ad with the highest bid for
Simplest algorithm is greedy It’s easy to see that the greedy algorithm
is actually optimal!
Complication 1: Ads with different CTRs
Each ad has a different likelihood of being clicked Advertiser 1 bids $2, click probability =
0.1 Advertiser 2 bids $1, click probability =
0.5 Clickthrough rate measured historically
Simple solution Instead of raw bids, use the “expected
revenue per click”
The Adwords InnovationAdvertiser Bid CTR Bid * CTR
A
B
C
$1.00
$0.75
$0.50
1%
2%
2.5%
1 cent
1.5 cents
1.125 cents
CTR: Click-Through Rate for that specific ad What fraction of times, when the ad is shown, do people click on the ad? SE has to track these statistics over time..
The Adwords InnovationAdvertiser Bid CTR Bid * CTR
A
B
C
$1.00
$0.75
$0.50
1%
2%
2.5%
1 cent
1.5 cents
1.125 cents
Complication 2: Advertisers bid on keywords not queries.. Many-to-Many correspondence between
queries and keywords purchased Select top-k similar ads, pick from among
them the one with highest bid*CTR Retrieving Similar Ads
Exact matching IR-style similarity Query rewriting (say using scalar/association
clustering) Followed by exact matching
Need inverted indexes
Complication 3: SE may want to show multiple ads per query First ad shown: Is the one that is most
similar and has the highest bid*CTR value
Second ad shown isn’t necessarily the one that has next highest bid*CTR.. Need to worry about inter-ad correlation
(diversity) Need to worry about user browsing
pattern (user may stop looking after the first ad)
Optimal Ranking given Abandonment
The physical meaning RF is the profit generated for unit consumed view probability of ads
Higher ads have more view probability. Placing ads producing more profit for unit consumed view probability higher up is intuitive.
Rank ads in the descending order of:
)()()()$()(aaRaRaaRF
17Optimal ranking considering
inter-ad correlation is NP-hard
Raju Balakrishnan
Complication 4: Advertisers have limited budgets Each advertiser has a limited budget
Search engine guarantees that the advertiser will not be charged more than their daily budget
SE needs to be smarter in selecting among the relevant advertisers so it is sensitive to the remaining budget..
Simplified model (for now) Assume all bids are 0 or 1 Each advertiser has the same budget B One advertiser per query Let’s try the greedy algorithm
Arbitrarily pick an eligible advertiser for each keyword
Bad scenario for greedy Two advertisers A and B A bids on query x, B bids on x and y Both have budgets of $4 Query stream: xxxxyyyy
Worst case greedy choice: BBBB____ Optimal: AAAABBBB Competitive ratio = ½
Simple analysis shows this is the worst case
Competitive Ratio: Ratio between online and off-line alg. Performance
BALANCE algorithm [MSVV] [Mehta, Saberi, Vazirani, and Vazirani] For each query, pick the advertiser with
the largest unspent budget Break ties arbitrarily
Example: BALANCE Two advertisers A and B A bids on query x, B bids on x and y Both have budgets of $4 Query stream: xxxxyyyy BALANCE choice: ABABBB__
Optimal: AAAABBBB Competitive ratio = ¾ In the general case, worst competitive
ratio of BALANCE is 1–1/e ~ 0.63
Generalized BALANCE Arbitrary bids; consider query q,
bidder i Bid = xi (can also consider xi * CTRi ) Budget = bi Amount spent so far = mi Fraction of budget left over fi = 1-mi/bi Define i(q) = xi(1-e-fi)
Allocate query q to bidder i with largest value of i(q)
Same competitive ratio (1-1/e)
Complication 4: How do we encourage Truthful Bidding? How to set the keyword bids?
I am willing to pay 2$/click on the phrase “best asu course”
But should I be charged the full 2$ even if no one else cares for that phrase? If I know that I will be charged my full bid
price, I am likely to under-bid I lose the auction; search engine loses
revenue Solution: Second Price Auction (Vickery Auction)
The advertiser with the highest bid wins, but only pays the price of the second highest bid Has the property that truthful bidding is
dominant strategy [No other strategy does better]
Insight: Physical auctions
Are second-price auctions!
Generalizing to multiple items
Two issues: 1. Allocation: who gets which item? Decided by Maximal Matching
2. Pricing: What price do they pay? Decided by opportunity cost introduced by the winner (which involves doing matching again without the winning bid)
(Each advertiser may bid on more than one query)
30
Q1
Q2
This opportunity cost for a bidder is defined as the total bids of all the other bidders that would have won if the first bidder didn't bid, minus the total bids of all the other actual winning bidders.
Generalizing to multiple items
Single item Vickery Auction(Truthful bidding is dominant strategy)
Multi-item VCG Auction(Truthful bidding is dominant strategy) --Price paid by each buyer is his/her externality (how much others would have benefited if he/she weren’t around)
Multi-item Generalized Second Price Auction(Truthful bidding is not a dominant strategy) Price paid by i-th bidder is the bid of i+1th bidder
Google (and most SE) use this
Two issues: 1. Allocationwho gets which item? 2. PricingWhat price do they pay?
What we swept under the rug.. We handled
user interests (CTR) search engine interests (ranking,
budgets) Advertiser interests (keyword bidding,
auctions) As if they are sort of independent..
But they are inter-dependent.. The full solution needs to bring them
all together.. ..and won’t have too many neat
properties that you can prove
