This document discusses online advertisement and the AdWords problem. It begins by describing traditional advertising like posters, magazines and billboards that charge per impression, then describes how online ads use targeting and metrics like clicks. It explains that AdWords is Google's advertising system where advertisers bid on keywords and the highest bidder's ad appears. The document defines the AdWords problem and algorithms like greedy and balance that aim to maximize revenue by sorting ads by expected revenue rather than bid. It provides an example showing the balance algorithm can achieve a better competitive ratio than greedy. Finally, it mentions implementing this on an open advertising dataset.

1. Online Advertisement and the
AdWords Problem
PRESENTER
RAJESH PIRYANI

2. Traditional Advertising
At the beginning: Traditional Ads
 Posters, Magazines, Newspapers,
Billboards.
What is being Sold:
 Pay-per-Impression: Price depends on
how many people your ad is shown to
(whether or not they look at it)

3. Advertising on the Web
Online Ads:
 Banner Ads, Sponsored Search Ads, Pay-per-Sale ads.
Targeting:
 Show to particular set of viewers.
Measurement:
 Accurate Metrics: Clicks, Tracked Purchases.
What is being Sold:
 Pay-per-Click, Pay-per-Action, Pay-per-Impression

4. Banner Ads
Banner ads (1995-2001)
Popular websites charged X$ for
every 1,000 “impressions” of the ad
Called “CPM” rate (Cost per
thousand impressions)
Low click-through rates
Low ROI for advertisers

5. Some Statistics
Adwords is Advertiser
AdSense is for Publisher

6. Ads Format

7. Textual Ads
Web Advertising covers large amount of internet environment.
Internet advertiser spend 17 billion dollars in US on advertising, with 20%
growth rate.
Textual ads covers huge portion of this market.
1. Sponsored Search or paid search advertising
2. Contextual advertising or context match

8. What is AdWords
Advertising system in which advertisers bid on certain keywords in order for their clickable
ads to appear in Google's search results

9. 9
Search Query:
‘flowers’
Google
Search Results

10. AdWords Ads

11. Understanding Adwords Technicalities
Keywords:
Ads are continuously matched to internet users interest based on your keywords
Use keywords match types to your advantage
 Broad Match
 Phrase Match
 Exact Match
 Negative Keywords
URL:
Two types of URL are used in an Adwords namely
Display URL
Actual URL
Broad Match
Keywords: buy flowers
Queries:
• buy flowers
• buy red flowers
• flowers buy
• New York buy flowers
Phrase Match
Keywords: “buy flowers”
Queries:
 Where can I buy flowers
 buy flowers in New Delhi
 buy red flowers (extra word)
 flowers buy (the words are
reversed)
Exact Match
Keywords: [buy flowers]
Queries:
 buy flowers
 Buy flowers (Capitalization
doesn’t matter)
Negative Match
Keywords: -cheap
Queries:
 buy cheap flowers
 cheap flowers in New York

12. Performance based advertising
Introduced by Overture around 2000
Advertisers bid on search keywords
When some one searches for that keywords, the highest bidder’s ad is shown
Advertisers is charged only if the ad is clicked on
Similar model adopted by Google with some changes around 2002
Called adwords

13. Adwords Problem Definition
Input
Advertisers Bid Set for each search query
CTR (Click through Rate) is provided for each pair of query-advertiser.
The budget of each advertiser for time period (it can be month, year
or depends).
Limit on the amount of ads to be illustrated for each search query.
𝑪𝑻𝑹 =
𝑵𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒄𝒍𝒊𝒄𝒌𝒔 𝒐𝒏 𝒂𝒅𝒔
𝑵𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒕𝒊𝒎𝒆𝒔 𝒕𝒉𝒆 𝒂𝒅 𝒊𝒔 𝒔𝒉𝒐𝒘𝒏

14. Adwords Problem Definition
Response for apiece query with advertisers set such that:
The resultant set of ads should not be larger than the provided limit.
Every advertiser has to bid on the query.
For each click on the ads, every advertiser has sufficient budget to pay for it.
𝑹𝒆𝒗𝒆𝒏𝒖𝒆 = 𝑏𝑖𝑑 ∗ 𝐶𝑇𝑅
𝑪𝒐𝒎𝒑𝒆𝒕𝒊𝒕𝒊𝒗𝒆 𝑹𝒂𝒕𝒊𝒐 =
minimum total revenue of algorithm for any sequence
𝑟𝑒𝑣𝑒𝑛𝑢𝑒 𝑜𝑓 𝑜𝑝𝑡𝑖𝑚𝑢𝑚 𝑜𝑓𝑓𝑙𝑖𝑛𝑒 𝑎𝑙𝑔𝑜𝑟𝑖𝑡ℎ𝑚 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑠𝑎𝑚𝑒 𝑠𝑒𝑞𝑢𝑒𝑛𝑐𝑒
The main objective: Maximize the search engine revenue

15. Expected Revenue
Advertiser Bid CTR Expected Revenue=Bid*CTR
A $1.00 1% 1 Cent
B $0.75 2% 1.5 Cent
C $0.50 2.5% 1.125 Cent

16. The Adwords Innovation
Advertiser Bid CTR Expected Revenue=Bid*CTR
B $0.75 2% 1.5 Cent
C $0.50 2.5% 1.125 Cent
A $1.00 1% 1 Cent
Instead of sorting advertisers by bid, sort by expected revenue!
• CTR of ad is unknown
• Advertisers have limited budgets and bid on multiple ads (Balance Algo.)

17. Greedy Algorithm
Simplified Environment
There is 1 ad shown for each query
All advertisers have same budget B
All ads are equally likely to be clicked
Value of each ads is the same (=1)
Greedy Algorithm
For a query, select any advertiser who value is 1 for that query.
Competitive Ratio=½.

18. Worst Scenario for Greedy Algorithm
Two Advertiser A and B
A bids on query x, B bids for both query x and y
Both have budget $4
Query Stream: xxxx yyyy
Worst case greedy choice: BBBB _ _ _ _
Optimal: AAAA BBBB
Competitive Ratio =1/2.
This is the worst case!

19. Balance Algorithm
This algorithm proposed by Mehta, Saberi, Vazirani, and Vazirani
For each query, to select the advertiser with the largest unspent budget
Breaks ties arbitrarily

20. Example
Two Advertiser A and B
A bids on query x, B bids for both
query x and y
Both have budget $4
Query Stream: xxxx yyyy
Balance Choice : ABABBB _ _
Optimal: AAAA BBBB
Competitive Ratio =3/4.
For balance with 2 advertisers
(_)
A B
4 4
q(x) A
A B
3 4
q(xx) B
A B
3 3
q(xxx) A
A B
2 3
q(xxxx) B
A B
2 2
q(xxxxy) B (NV: A)
A B
2 1

21. Implementation

22. Dataset
Open Advertising dataset
Keywords List
Bidding List: US and UK Market
Bidding dataset1: 177 US Market, 179 UK Market
Bidding dataset2: 244 US Market, 244 UK Market
Bidding dataset3:
Web Page results associated with keywords

23. Block Diagram
Keywords List Bidding Info
Webpages
System CTR Calculation
Results
Query

24. Implementation on Open Advertising
Dataset

25. Implementation on Open Advertising
Dataset

26. Reference
Book:
Mining Massive Dataset
By Jure Leskovec, Anand Rajaraman, Jeff Ullman
Papers:
A. Broder, M. Fontoura, V. Josifovski, and L. Riedel, “A semantic approach to
contextual advertising,” Proceedings of the 30th annual international ACM
SIGIR conference on Research and development in information retrieval - SIGIR
’07, 2007.
T.-K. Fan and C.-H. Chang, “Blogger-Centric Contextual Advertising,” Expert
Systems with Applications, vol. 38, no. 3, pp. 1777–1788, Mar. 2011.

27. Thank you