recent advances in computational advertising

Recent advances in computational advertising: design and analysis of ad retrieval systems

Evgeniy [email protected] @y

1

What is “Computational Advertising”?

• A new scientific sub-discipline that provides the f d i f b ildi li d i l l ffoundation for building online ad retrieval platforms– To wit: given a certain user in a certain context,

find the most suitable ad

• At the intersection of Large scale text analysis– Large scale text analysis

– Information retrieval– Statistical modeling and machine learningStatistical modeling and machine learning– Optimization– Microeconomics

© Yahoo! Research 2010 2Technologies described might or might not be in actual use at Yahoo!

Computational Advertising at Yahoo! ResearchYahoo! Research

© Yahoo! Research 2010 3

Online advertising spending


Textual advertising

1 Ads driven by search keywords –1. Ads driven by search keywords Sponsored Search (a.k.a. “keyword driven ads”, “paid search”, etc.), p , )

2. Ads directly driven by the content of a web page – Content Match (a k a “contextpage – Content Match (a.k.a. context driven ads”, “contextual ads”, etc.)

Textual advertising on the Web is strongly related


to NLP and information retrieval

Sponsored searchText based ads driven by a keyword searchText-based ads driven by a keyword search


Content match adsText-based ads driven by the page contentText-based ads driven by the page content

C t tContent match

ads


Anatomy of an ad

Bid phrases: {SIGIR 2010,computational advertising

Title

computational advertising, Evgeniy Gabrilovich, ...}Bid: $0.10

Title

CreativeDisplay URLDisplay URL

Landing URL: http://research.yahoo.com/tutorials/sigir10_compadv

© Yahoo! Research 2010 8Landing page

So when do advertising dollars actually change hands?actually change hands?

CPM t th d i i– CPM = cost per thousand impressions• Typically used for graphical/banner ads

(brand advertising)

– CPC = cost per clickp• Typically used for textual ads

CPT/CPA = cost per transaction/action– CPT/CPA = cost per transaction/action a.k.a. referral fees or affiliate fees


Beyond keyword matching

• Matching ads is relatively simple for explicitly bid keywordsWhat about queries on which there are no bids ?– Advertisers should be able to bid on “broad queries” and/or

“concept queries”– Advertisers need volume – the total amount of searches on bid

phrases is not enough !

• Suppose your ad is “Good prices on Seattle hotels”• Suppose your ad is Good prices on Seattle hotels• Naïve approach: bid on any query that contains the word Seattle• Problems

• “Seattle's Best Coffee Chicago”

• “Alaska cruises start point”


• Ideally: bid on any query related to Seattle as a travel destination

The old school: heuristic ad matchingheuristic ad matching

• Sponsored searchp– Exact match between the query and the bid phrase

of the ad (modulo simple normalization, e.g., stemming)stemming)

– Advertisers cannot possibly bid on all relevant queries (especially rare ones)

• Use advanced match (e.g., through query-to-query rewrites)

• Content match– Extract bid phrases from pages, thus reducing the

problem to exact match Both essentially perform record lookup


Both essentially perform record lookup

The old school (cont’d)

Query Abbey Road

Front end

Query rewriting moduleSimplistic

lyrics

QueryQuery rewrites

query expansion

Exact matchIgnoring (or underusing) the multitude of information

il bl Candidate ads

Revenue d i Ad slate

available


reordering Ad slate

The new approach: knowledge-based ad retrievalknowledge based ad retrieval

• Ad indexing and scoring based on all the information• Ad indexing and scoring based on all the information available (bid terms, title, creative, URL, landing page, ...)– Similar to document indexing in IR

• Use standard IR tools (text preprocessing – tokenization, stemming, entity extraction; inverted indexes etc.)

– Use multiple features of the query and the ad

• Elaborate query expansion

2nd l d i ( ki )• 2nd pass relevance reordering (re-ranking)– Using features not available to the 1st pass model (e.g., set-level

features, click history)


The new approach (cont’d)

Query Miele

Front end

Ad query<Miele, appliances, kitchen,“appliances repair” “appliance parts”Ad query

generation

Ad query

appliances repair , appliance parts ,Business/Shopping/Home/Appliances>Rich query

Fi

Ad search engineThe hidden parts of ads (bid phrases +

First pass retrieval

Relevance

landing pages) allow us to augment the ads (cf. query


Candidate ads

Revenue reordering Ad slate

Relevance reorderingexpansion)

Research questions

Should we show ads

How to select How to

index thequestions show ads at all?relevant

ads?

index the ad corpus?Can we generate bid

phrases (or even entire ad campaigns)

automatically?

Wh t i thWhat is the interplay between the organic and

sponsored

Sh ld

presults?

Can we optimally

Should we use the

landing for indexing?


p ychoose the

landing page?

g

How to select

relevant ads?

Feature generation for improved ad retrieval

(SIGIR 2007 B d t l(SIGIR 2007, w. Broder et al.;ACM TWEB 2009, Gabrilovich et al.))


Query classification using Web search resultsWeb search results

• Humans often find it hard to readily see what the yquery is about … – But they can easily make sense of it once they look at

th h ltthe search results…• Let computers do the same thing

Infer the q er intent from the top algorithmic search– Infer the query intent from the top algorithmic search results (“pseudo relevance feedback”)

• Classify search results (either summaries or full pages)• Let these results “vote” to determine the query class(es) in a

large taxonomy of commercial topics• Our goal: Construct additional features to retrieve better ads


Our goal: Construct additional features to retrieve better ads

Example: ex560lku

CATEGORIES1. Computing/Computer/ Hardware/Computer/Peri-pherals/ComputerModems


If we know it is about actiontec usb modem then we have plenty of ads …p y


Our approach

Traditional approach:

Query Classifier Insufficient data

Our approach:

Query Search engine

Very large scale

y

Using Web

Search results Pre-classify all pages just once !


ClassifierUsing Web as external knowledge

just once !

Research questions

Snippets or

Number of search results tofull pages? results to obtain

N b fNumber of classes per search result

Aggregation:

bundling or voting?


bundling or voting?

The effect of using Web search results


B d th b fBeyond the bag of words: matching textual ads in thetextual ads in the enriched feature space

(SIGIR 2007, Broder et al.;( , ;CIKM 2008, w. Broder et al.)


What can we do about non-English queries ?(iNEWS @ CIKM 2008, w. Wang et al.;WSDM 2009 W t l )WSDM 2009, w. Wang et al.)

• Developing a taxonomy and building a query classifier for every language is prohibitively expensive

• Solution: apply off-the-shelf MT to the search results in the source languageg g

Very short

Machine Translation

Very short text Sufficiently

long text


The effect of query expansion prior to applying MTprior to applying MT.

The gap for infrequent queries is wider

Baseline = translate th ( i MT)the query (using MT), then classify the result as an English query


more frequent less frequent(Head) (Tail)

How to index the

ad corpus?

The Anatomy of an ad: Structured indexing and retrieval

for sponsored search(WWW 2010 w Bendersky et al )(WWW 2010, w. Bendersky et al.)


Structure of online ad campaigns: the ad schemaad schema

Advertiser

New Year deals on

Account 1 Account 2 …

New Year deals on lawn & garden toolsBuy appliances on

Black Friday

Campaign 1

Campaign 2 …Kitchen appliances

Ad group 1

Ad group 2 …

Creatives Bid phrasesAd

Brand name appliances { Miele,

Can be just a single bid phrase, or

thousands of bid phrases (which are


Compare prices and save moneywww.appliances-r-us.com

KitchenAid, Cuisinart, …}

phrases (which are not necessarily

topically coherent)

Implications of the campaign structurestructure

• What is the appropriate indexing unit?g– Cartesian product of creatives and bid phrases? Ad group?

• Leveraging information from higher levels to address data sparsity at children nodesat children nodes

• What is the right approach to document length normalization?– Large variability of document lengths– Probability of shorter documents (smaller ad groups) to be retrieved is

higher than their probability of being relevant

• How to index and score templated ads?p

• Prior work mostly considered ads as independent atomic units and ignored hierarchical campaign structure


g p g

Possible approaches

1. Term index (Cartesian product of all creatives and bid terms)• Huge index, small focused documents

2. Creative index (a creative is coupled with all the bid terms in the ad group)the ad group)

• Two-stage retrieval (first choose the creative, then pick the term)• Bid terms are duplicated across creatives

3. Ad group index• Indexing units are entire ad groups• Three stage retrieval (first choose• Three-stage retrieval (first choose

the ad group, then the creative, and finally pick the term)M t t i d


• Most compact index

Retrieval speed vs. relevance

Term index yields most relevantTerm index yields most relevantads, yet is least efficient (20x slower than the ad group index)

Are we tradingAre we trading effectiveness

for efficiency ?for efficiency ?

Ad group index is most efficient(2x faster than creative index), yet least effective


least effective

Using learning to rank techniques:structured re-rankingstructured re ranking

• Step 1: Retrieve an initial set of candidates using the ad group index

• Step 2: Re-rank the candidate set using structural features (instead of ignoring the structure and scoring creatives and terms independently)– Ad group score, creative-term pair scoreg p , p– # bid terms in the ad group– Unigram entropy (cohesiveness)

of the ad group– Ratio of query words covered

by the ad group text– Fraction of the titles / terms /

URLs that contain at least one query term

– Other features are possible !

© Yahoo! Research 2010 31feature functions

Re-ranking retrieval performance

nDCG@5 Len 1 (143 i )

Len 2-3 (443 i )

Len 4+ (187 i )(143 queries) (443 queries) (187 queries)

Term index 0.841 0.716 0.656

St t d 0 849 0 731 0 686Structured re-ranking

0.849(+ 0.95%)

0.731(+ 2.1%)

0.686(+ 4.6%)

• Structured re-ranking is superior for all query lengths

• Most notable improvements are obtained for longer queries


• Still very efficient!

To swing or not to swing: learning when (not) to advertise (CIKM 2008, w. Broder et al.)to advertise (CIKM 2008, w. Broder et al.)

• Repeatedly showing non-Should we show ads Repeatedly showing non

relevant ads can have detrimental long-term effects

at all?

• Want to be able to predict when (not) to show individual ads or a set of ads (“swing”)ads or a set of ads ( swing )

• Modeling actual short- and flong-term costs of showing

non-relevant ads is very difficult


Thresholding approach

• Decision made on individual ads based onDecision made on individual ads based on ad scores

Set a global score threshold– Set a global score threshold– Only retrieve ads with scores above it– If none of the ad scores are above the

threshold, then no ads are shown (“no swing”)

• Scores are not necessarily comparable across queries!


q

Machine learning approach

• Decision made on sets of ads based on aDecision made on sets of ads based on a variety of features

Learn a binary prediction model (“swing” /– Learn a binary prediction model ( swing / “no swing”) for sets of ads

– If we swing then all ads are retrievedIf we swing, then all ads are retrieved– If we do not swing, then no ads are retrieved

F t d fi d t f d th• Features defined over sets of ads, rather than individual ads


Features

• Relevance features– Word overlap, cosine similarity between ad and query/page

• Vocabulary mismatch features– Translation models– PMI between query/page terms and bid terms

• Ad-based features– Bid price (higher bids may indicate better ads)p ( g y )

• Result set cohesiveness features– Coefficient of variation of ad scores (std/mean) – Result set clarityResult set clarity

• If the set of ads is very cohesive and focused on 1-2 topics, the relevance language model is very different from the collection model

– Entropy


Entropy

Wh t h ft d li k?What happens after an ad click? Quantifying the impact of landing y g p g

pages in Web advertising(CIKM 2009 B k t l )(CIKM 2009, w. Becker et al.)

Can we optimally choose the

landing page?


g p g

Conceptually: context transfer

Search engine result page

Click!

g p g

Landing page

User’s activity thon the

advertiser’s Web siteConversion

(e.g., purchase of the


(e.g., purchase of the product or service being advertised)

All landing pages are not created equal(and neither are the corresponding conversion rates)(and neither are the corresponding conversion rates)

• We propose a concise taxonomy of landing page types:I. Homepage (25%) – top-level page of the advertiser’s site

(e.g., Verizon.com)II. Category browse (37.5%) – main page of a sub-section ofII. Category browse (37.5%) main page of a sub section of

the advertiser’s site, which describes a category of related products

III. Search transfer (26%) – search within the advertiser’s site ( )OR on other Web sites

IV. Other (11.5%) – terminal pages (e.g., promotion pages or forms)


Examples: Homepage


Examples: Category browse


Examples: search transfer


Landing page classifier

• Features: bag of words, HTML patterns[ST] “ h lt ” “f d”– [ST] “search results”, “found”

– [CB] “Home > Verizon > LG phones”– [HP] HTML overlap between given URL and base URL– [O] ratio of form elements to text, few outgoing links

• Accuracy on the pilot dataset (10-fold xval): 83%• Accuracy on additional 100 labeled pages: 80%

• Distribution of landing page types in a set of 20,000 g p g yplanding pages from Yahoo! Toolbar logs:

Homepage Search Transfer

Category Browse

Other


Transfer Browse34.4% 22.3% 36.0% 7.3%

Using the landing page taxonomy

Picking the right landing page type for each ad

Improving the conversion rateImproving the conversion rate

Improving advertisers’ ROI !


Landing page type usage vs. conversion: breakdown by query frequency

Navigational queries

Category and search transfer become more

popular for rare queriesp p q


Observed conversion rates are in sharp contrast with usage frequency

of the different page types

Landing page type usage vs. conversion: breakdown by query priceb ea do by que y p ce

Category and search transfer are dominant for cheaper queriesp q


As the price goes up, so does the conversion rate (higher quality pages?)

What is the interplay between p ythe organic and

sponsored results?

Competing for users’ attention:On the interplay between organic andOn the interplay between organic and

sponsored search results(WWW 2010 w Danescu Niculescu Mizil et al )(WWW 2010, w. Danescu-Niculescu-Mizil et al.)


The interplay between ads and organic resultsorganic results

“... in an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is thatdearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among theneed to allocate that attention efficiently among the overabundance of information sources that might consume it.”

-- Herbert Simon, “Designing Organizations for an Information-Rich World”, 1971.,

• Is there competition for clicks between ads and organic results ?• Do users prefer ads that are similar to the organic results, or do

they prefer diversity ?they prefer diversity ?

We found that the nature of this interplay depends on the type of the query


on the type of the query

Relation between the CTR of ads and the CTR of organic resultsand the CTR of organic results

• Negative correlation (competition)g ( p )– Users are only willing to spend limited time and effort on

each query

P iti l ti (d d th lit f• Positive correlation (depends on the quality of results)– Easy query (“online radio”) – decent ads and organicEasy query ( online radio ) decent ads and organic

results – clicks on both– Hard query (“who is giving this talk?”) – poor results on

both sides – no clicks on eitherboth sides no clicks on either

• Independence (null hypothesis)– Users consider ads and organic results as two


gindependent sources of information

Findings: competition + positive correlationcompetition + positive correlation


Decoupling the forces

• Users are willing to invest limited effort in geach query competition

• In order to single out the competition effect, we gtried to explicitly model the amount of effortthe user is willing to investL ff i i l i [B d 2002]• Low effort = navigational queries [Broder, 2002] (27% of queries)

“Pandora radio” “Bank of America”– Pandora radio , Bank of America

• High effort = non-navigational queries“Meaning of life” “academia vs industry”


– Meaning of life , academia vs. industry

Competition clearly exists for navigational queriesnavigational queries

We also examined differentWe also examined different degrees of navigationality:

the less navigational the query is, the less competition we

observed


Another viewpoint:Do users prefer ads that are more similar to the organic results or more diverse ads?the organic results or more diverse ads?

• Both have been argued for in prior workBoth have been argued for in prior work• Preference for similarity

– Ads are more likely to be relevant– This assumption is often made in query

i f d ti i [B d t l 2008]expansion for advertising [Broder et al., 2008]

• Preference of diversity– Diversity among organic search results has

often been shown to be desirable (e.g., entire i di it @ WWW 2010)


session on diversity @ WWW 2010)

We found evidence for users’ preferring both diversity and similaritybot d e s ty a d s a ty

So we need to dig deeper

again ...

Overlap measured using the Jaccard

coefficient


between titles of ads and organic

results

Let’s break down by navigationality againby navigationality again


Break down by navigationality (cont’d)(cont d)


Counterintuitive ?


Responsive and incidental ads

• Responsive ads directly address the user’sResponsive ads directly address the user s information need

Incidental ads are only somewhat related to the– More likely to be similar to the organic results

• Incidental ads are only somewhat related to the user’s information need– Unreasonable as organic results but ok for adsUnreasonable as organic results, but ok for ads

• Example: query = “free internet radio”

– More likely to be different from the organic results

• Example: query = free internet radio– Responsive: “Pandora Internet Radio”– Incidental: “Discount Bose Computer Speakers”


– Incidental: Discount Bose Computer Speakers

Now it all make sense ...

Using the featuresUsing the features that quantify this

interplay, we improved the accuracy of CTRaccuracy of CTR prediction by 5%


Summary

1 The financial scale is huge1. The financial scale is huge2. Advertising is a form of information3. Finding the “best ad” is an information

retrieval problem Multiple, possibly contradictory utility functions Classical IR needs significant adaptation

4. The optimal solution requires extensive use of external knowledge


g

Th k !Thank [email protected]

http://research.yahoo.com/~gabr

61

This talk is Copyright Yahoo! 2010.Y h ! d th A th t i ll i ht i l diYahoo! and the Author retain all rights, including

copyright and distribution rights. No publication or further distribution in full or in part is permitted

without explicit written permission.

The opinions expressed herein are the responsibilityThe opinions expressed herein are the responsibility of the author and do not necessarily reflect the

opinion of Yahoo! Inc.

This talk benefitted from the contributions of many colleagues and co-authors at Yahoo! and elsewhere.


Their help is gratefully acknowledged.

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