Table Of ContentBudget Constraints and Optimization
in Sponsored Search Auctions
Intelligent Systems Series
Editor-in-Chief
Fei-Yue Wang
Chinese Academy of Sciences
Contact information: [email protected]
Editorial Board Members
Jim Hendler: Rensselaer Polytechnic Institute
William T Scherer: University of Virginia
Hsinchun Chen: University of Arizona
Kathleen Carley: Carnegie Mellon University
Ruwei Dai: Chinese Academy of Sciences
Youxian Sun: Zhejiang University
Chelsea (Chip) C White: Georgia Tech
Petros Ioannou: University of Southern California
Frank Lewis: University of Texas at Arlington
Bruno Sicilianos: Università degli Studi di Napoli Federico II
Wendy Hall: University of Southampton
Budget Constraints and Optimization
in Sponsored Search Auctions
Yanwu Yang
Fei-Yue Wang
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Preface
Nowadays, more and more people use search engines as their main entrance to the Web
and this offers fertile soil for sponsored search auctions to evolve into one of the most
prominent online advertising channels. Search advertisements have also become the primary
revenue source for search engines. The growing prosperity of search advertising markets is
vastly driven by the influx of millions of advertisers. The search advertisers, especially those
from small corporations, usually face budget constraints due to their financial conditions. On
the one hand, budget constraints significantly influence advertisers’ strategies in sponsored
search auctions. On the other hand, one of the most difficult tasks for advertisers is how to
effectively determine and allocate the optimum level of advertising budget, in order to survive
in fierce competition and maximize profits.
The budget is an endogenous factor in sponsored search auctions that heavily influences other
advertising strategies. There have been some research efforts regarding budget-related deci-
sions in sponsored search auctions. Most either take the budget as the constraint for other
advertising strategies, or allocate the budget to keywords. However, these works are not
directly and operationally suitable for practical paradigms because they ignore the search
advertising structure, game rules, and functionality provided by major search engines. As
observed by some empirical studies, improving the profit by better allocation strategies is
much more effective than improving the overall budget.
There are many challenges associated with budget decision-making in sponsored search
auctions. First, the complex structure of search auction mechanisms (e.g. GSP) together with
a high volume of search demands leads to continual dynamics in auction processes, which
offers serious challenges to various decision-making activities in sponsored search auctions.
Secondly, large uncertainties exist in the mapping from the search advertising budget to the
expected profit. Thirdly, the budget is structured in the entire lifecycle of search advertising
activities, rather than a simple constraint as in traditional economic phenomena. This makes
budget decisions in sponsored search auctions a structured set of optimization problems.
Thus, a great concern of advertisers in sponsored search auctions is not only to obtain local
optimizations but a global optimal outcome with structured budget constraints.
xi
Preface
Budget-related decisions in sponsored search auctions are recognized as an essentially
structured decision problem, rather than a simple constraint. With consideration of the
entire lifecycle of advertising campaigns in sponsored search auctions, budget decisions in
sponsored search auctions occur at three levels: allocation across search markets, temporal
distribution over a series of slots (e.g. day), and adjustment of the remaining budget (e.g. the
daily budget). Therefore, we argue that it is of necessity to propose a novel framework for
budget optimization in sponsored search auctions, taking into account the entire lifecycle of
advertising campaigns. In this book we take the initiative to develop a hierarchical budget
optimization framework (BOF) with consideration of the entire lifecycle of advertising
campaigns in sponsored search auctions. The BOF framework could support a set of
close-loop strategies across different levels of abstractions (e.g. system, campaign, and
keyword), thus providing an environment/testbed for various budget strategies. Following
the BOF framework and identified principles, we explore a spectrum of budget decisions at
different levels of abstraction, and in different temporal granularities. Finally we shed some
light on some interesting but challenging research prospects in budget-related issues in
sponsored search auctions, and the joint optimization of advertising strategies.
We would like to thank our colleagues and graduate students in the ISEC research group
(Internet Sciences and Economic Computing), and the State Key Laboratory of Management
and Control for Complex Systems for their great assistance in the completion of this book.
We also thank Prof. Yanqing Gao and Jiaying Xu for their support, and Ms. Juanjuan Li
and Rui Qin for their hard work and great patience with the editing process. The
publication of this book is partially supported by the National Natural Science Foundation
of China (Nos. 71071152, 71272236, 60921061).
Yanwu Yang
Fei-Yue Wang
Chinese Academy of Sciences
Beijing, China
xii
CHAPTER 1
Search Engine Meets Economics
1.1 The Web and Search Engines
1.1.1 TheWeb
TheWorldWideWeb(WWW)isarepositoryofinterlinkedhypertextdocuments,andknown
astheinformationsuperhighway.WithaWebbrowser,peoplecanaccessvariouskindsof
Webpagescontainingtext,images,videos,andothermultimedia,andnavigatefollowing
hyperlinks.TheadventoftheWebhaschangedthewaythatpeoplecommunicatewitheach
other,perceiveandinteractwiththeenvironment.Theinformationcanbedisseminatedfaster
andfasteraroundtheglobe.Thegoalattheinitialstageisthatahumanusercanseamlessly
surftheexistingWorldWideWebandtheemergingWebservices,andeasilycomposeand
invokeWebservicesontheflywithoutbeingasoftwareengineer(Agarwa,2003).Withthe
developmentoftheWeb,surfingtheWebhasbeenrealized.Asubstantialamountof
informationhasbeenprovidedwhich,tosomedegree,enablesWebuserstocomprehensively
andquicklygrabaccurateinformationandknowledgeontheWeb.
TheInternetandtheWebhavebeentransformingourmodernsociety,andsearchenginesplay
animportantroleinthisprocess(LawrenceandGiles,1999).Websearchbehaviorinvolves
notonlydeterminationofterms,butalsotheconstructionofqueriesandselectionof
informationentriesusingsearchengines(EastmanandJansen,2003).Also,howpeople
searchandinteractwiththeWebandwhattheyaresearchingfor,“thewhy”ofusersearch
behavior,isactuallyessentialtounderstandauser’sinformationneeds(RoseandLevinson,
2004).Mostsearchenginesdesignprocessesandalgorithmstoprovidesearchresultsto
satisfyusers’information.TheWebuser’sinformationneedscanbeclassifiedintodifferent
states,namelyinformational,navigational,andtransactional(Broder,2002).Roseand
Levinson(2004)describedaframeworkforunderstandingtheunderlyinggoalsofusersearch
behavior,andfoundthatnavigationalsearchesarelessprevalentthangenerallybelieved,
whileapreviouslyunexploredresourceseekinggoalmayaccountforalargefractionofWeb
YangandWang:BudgetConstraintsandOptimizationinSponsoredSearchAuctions.http://dx.doi.org/10.1016/B978-0-12-411457-9.00001-X
©2014ElsevierInc.Allrightsreserved.
1
2 Chapter1
searches.Thisencompassestherequirementfornovelwaysofonlinesearching,information
retrievalandfiltering,anduserinterfaces.
Bytakingusers’interestsandpreferencesintoaccount,Webpersonalizationexploresthe
explicitandimplicitneedsofuserstoprovidecustomizedservicestoanindividual(orgroup)
Webuser(Yang,2005,2007;Yangetal.,2010).Statisticscollectedbysearchenginesand
systemsontheWebshowthatspatialinformationispervasiveontheWeb,andthatmany
queriescontainimplicitspatialfactors.However,itisnotstraightforwardtoprocess
location-aware(orlocation-driven)queries,especiallywhentheyareessentiallycontext-aware
(Bolletal.,2008).Yangetal.(2012a)introducedanintegratedframeworkleveragingspatial
knowledgeandsemanticconsiderationsconcerningthetargetplacesthatusersplantovisit.
Thisframeworkcansupportuserpreferenceelicitationandaspectrumofpersonalizedsearch
strategiesappliedtospatialinformationretrievalontheWeb(Yang,2006).
1.1.2 SearchEngines
SearchenginesaredesignedtohelpWebuserssearchforusefulandrelevantinformationon
theWeb.Webusersshouldbecapableofdistinguishingusefulinformation.WhenaWebuser
queriesacertainkeyword,searchengineswillreturnorganicsearchresultsthrough
informationretrievalanddatabasetechniques.Thesearchresultsarepresentedasalistonthe
pagereferredtoassearchengineresultspages(SERPs).Thesearchresultsusuallyconsistof
Webpages,images,andothertypesoffiles.Somesearchenginescanextractinformationand
knowledgeavailableindeep-Webdatabasesandopendirectories.UnlikeWebdirectoriesthat
aremaintainedmainlybyhumaneditors,searchenginescollect,store,processandmaintain
real-timeinformationthroughvariousalgorithms.
AccordingtoChitika,majorsearchengines(includingGoogle,Yahoo,Bing,andBaidu)
occupy98.5%ofthesearchenginemarket.Inparticular,Googletakesfirstplaceinthesearch
enginemarket,anditsleadingadvantagewillbekeptinthefuture.
Searchenginesmakeinformationaccessmoreconvenient,andmoreandmoreWebuserstake
searchenginesasthemainentrancetotheWeb.ArecentPewInternetstudyfoundthatsearch
enginesareusedby92%ofInternetusers.“Search”hasbeenthemostimportantbehaviorof
Internetusers.AccordingtoForresterResearch,thenumberofglobalsearchusersincreasesat
arateof10%ayear,whichishigherthanthe8%riseinthenumberofnewInternetusers.It
alsopointedoutthatwhateverthelanguagethatsearchusersuse,theywouldlikeonlytoclick
onthefirst10results,andupto99%ofsearchuserswillnotviewmorethan
30results.
Themainpurposeofsearchusersistoquicklygrabtargetedinformation.Thussearchengines
havetoestimatetheuser’srequirementstoprovidetherightresultsattherightmoment
accordingly.Users’behavioraltrailscanbeexploitedtoanalyzetheirrequirementsandthen
SearchEngineMeetsEconomics 3
determinethetargetedresults.Googleclaimedthatitwillimproveitssearchfunctiontomake
itmoreintelligent.Thisisexpectedtohelpunderstandsearchusersbetter,andtoprovide
moredirectanswers,inordertoimproveefficiencyandeffectiveness.
Theusefulnessofasearchenginedependsontherelevanceoftheresultsreturnedtosearch
users.ThereareplentyofWebpagesthatincludeaparticularwordorphrase;amongthem
somepagesaremorerelevant,popular,andauthoritativethanothers.Howasearchengine
determineswhichpagesarethebettermatches,andbywhichordertheseresultsshouldbe
presented,varieswidelyindifferentsearchengines.
Whensearchenginesstarttousehyperlinkanalysis,Web-positioningcompaniesmanipulate
hyperlinkstoboosttherankingoftheirclients’pages.Thishinderstheabilityofsearch
enginestoreturnhigh-qualityresultstosearchusers.Inordertoalleviatethisproblem,major
searchengineshaveputconsiderableeffortintodevelopingmoresophisticatedtechniquesthat
arekeptasbusinesssecrets(Henzinger,2001).Moreover,rankingalgorithmsarechangedand
optimizedwithendlesseffortovertime.
Besidesthesegenericsearchengines,specializedsearchenginesareusefulforWebuserswith
specialneeds,suchascommercialbusiness,musicandvideo.Also,mobilesearchhasbeen
developedtoenablesearchuserstolookforinformationthroughmobiledevicesanywhere,
anytime.Withthedevelopmentofsocialnetworks,thesocialsearchengineemergesasanew
typeofsearchengine.HorowitzandKamvar(2010)declaredthatintraditionalsearchengines
peopleusekeywordstosearchandtheknowledgebaseiscreatedbyasmallnumberof
contentpublishersbeforethequestionsareasked;thusthetrustisbasedontheauthority,
whileinasocialsearchenginepeopleusenaturallanguagestoaskquestions,answerscanbe
generatedinreal-timebyanyoneinthecommunity,andthenthetrustisbasedonintimacy.
1.2 Internet and Search Economics, Search Engine Marketing
1.2.1 InternetandSearchEconomics
TheInternethasgreatlychangedthewaythatpeopleperceiveandinteractwithsociety,as
withtheeconomy.TheInternetcanneverbeseparatedfromtheeconomy,anditsevolution
goesalongwitheconomicinnovations.Firstly,theInternetandrelatedtechnologieshave
madethecostsofmanykindsofmarketinteractionsplummet(BorensteinandSaloner,2001).
Secondly,theInternethasfacilitatedthecreationofnewmarketscharacterizedbyimproved
measurement,increasedcustomization,rapidinnovation,andmoreconsciousmarketdesign
(Levin,2011).Insummary,theInternetnotonlyencouragesthedevelopmentoftraditional
businesses,butalsoshapesanewtypeofeconomy,whichistermedthe“Interneteconomy.”
TheInterneteconomymanagesbusinessthroughmarketsontheInternetortheWeb.The
Interneteconomydiffersfromthetraditionaleconomyinseveralaspects,including
4 Chapter1
communication,marketsegmentation(Jansenetal.,2011),distribution,cost,andprice.
ElectroniccommerceisatypicalInterneteconomicform,whichreferstothebuyingand
sellingofproductsorservicesthroughelectronicsystemssuchastheInternetandother
computernetworks.ElectroniccommercecombinestraditionalbusinesswiththeInternetto
createmorevaluewhileconsuminglessresources,sincetheInternet,e.g.socialnetworks,
enablese-commercetoparticipateactivelyinonlineword-of-mouthcommunication
(Zhangetal.,2011;Jansenetal.,2009).
InterneteconomicscanbeconsideredasthestudyofthemarketsforvariousInternetservices
(McKnightandBailey,1997).Withsomeadvancedinformationandcommunication
technologiestomaketheInternetwork,economicandpolicyissuesmustbeaddressedto
sustaingrowthandtoexpandthescopeoftheInternet.Thishasledtotheemergenceofthe
fieldofInterneteconomicsasasubjectofbothacademicandindustryresearch.
SomeprinciplesofInterneteconomicshavebeendevelopedbycollaborativeeffortsamong
economists,engineers,andthosefromotherfields.McKnightandBailey(1997)believedthat
thereareatleastfiveareaswherenewsolutionstoInterneteconomicproblemsmightbe
explored.
1. Newempiricalwork,especiallywithrespecttouserbehavior(Xuetal.,2010).
2. Congestioncontrolprotocolsanduserfeedbacksinconcertwithoneanother.
3. Pricingasacongestioncontrolmethod(oraspartofaninterconnectionsettlements
process)inconcertwithenhancementstotheTCP/IPprotocolsuite.
4. IncreaseddatacollectionacrossInternetserviceproviderstoprovideanunderstanding
aboutthegrowthoftheInternetandarichdatasettodevelopeconomicmodelsand
increasetheunderstandingofthesubtleinteractionbetweenInternetengineering,
economics,andculturalpractices.
5. Furtherresearchonthedevelopmentoftechnologiesandpoliciesforinformationsecurity
forelectroniccommerceandprivacyprotection.
MostcurrentresearchonInterneteconomicstendstoanalyzeusage-sensitivepricingmodels
inonlineservices.MacKie-MasonandVarian(1995,1993)thoughtthatusage-sensitive
pricingwasthemostpromisingmechanismforaccomplishingprioritizationandsocially
efficientcongestioncontrol.Theyalsodescribedapossiblesmart-marketmechanismfor
pricingcongestionontheInternet.
Withtheprosperityofsearchengines,thesearcheconomy,asaspecialInterneteconomy,has
attractedalotofattention,bothfromacademicandindustrialfields.Thesearcheconomyis
mainlyreferredtoasvariouseconomicactivitiesassociatedwithsearchenginemarketing.It
impliesthatthesearcheconomyisarelevantactivitywherebysearchengineshelpwebsitesto
improveawarenessandthenproductandserviceexchanges.Searcheconomicsstudiesthe
implicationsoffrictionsinindividualbehaviorandmarketperformance,duetoimperfect
informationaboutexchangepossibilities.Thekeyimplicationofthesefrictionsisthat
