Textual Analysis Techniques to Perform Onlibne Advertising

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OUTLINE OF THE TALK

Introduction

Online Advertising

A Modern Contextual Advertising System

Syntactic Textual Analysis

Semantic Textual Analysis

Matching

An Example: ConCA

Experimental Results

Conclusions

References

Eloisa Vargiu ([email protected]) – Cagliari, 6 September 2012

INTRODUCTION

OUTERNET & INTERNET


OUTERNET & INTERNET

In Atkinson’s view something is missing…


ONLINE ADVERTISING

ONLINE ADVERTISING

Sponsored Search


ONLINE ADVERTISING

Banner Advertising


ONLINE ADVERTISING

Contextual Advertising


CONTEXTUAL ADVERTISING

Webpage Ad


ONLINE ADVERTISING

Is it always a good thing?


A MODERN CONTEXTUAL

ADVERTISING SYSTEM

A MODERN CONTEXTUAL ADVERTISING SYSTEM


SYNTACTIC TEXTUAL ANALYSIS

Text Summarization

Bag of Words Representation



Text summarization

State of the art techniques

First and Last Paragraph (FLP)

Title, First and Last Paragraph (TFLP)

Snippet (S)

Title and Snippet (TS)



First and Last Paragraph (FLP)

You don’t need to shell out thousands,

survive various ballots, or swap a family

member for a ticket to enjoy the 2012

Summer Olympic Games this year. There's

all manner of free events and associated

shenanigans taking place in London and

across the UK to mark the occasion. Here

are ten ways to join in without spending any

money.

http://www.roughguides.com/website/Travel/SpotLight/ViewSpotLight.aspx?spotLightID=575

Indulge in a family feast

Volunteer chefs at 24 Sure Start Centres

across the UK are preparing to dish up free

delights throughout the period. Details,

along with all the other events that make up

the Cultural Olympiad, are available on the

site.












money.







site.












money.







site.

London 2012 – Ten ways to celebrate the Olympics for free


Snippet (S)



Title and Snippet (TS)



Bag of Words (BoW) representation

Dimensionality reduction

Stop-words removal

Stemming

Vector representation

Set of pairs <word, occurrences>



Stop-words removal


survive various ballots, or swap a

family member for a ticket to enjoy the

2012 Summer Olympic Games this

year. There's all manner of free events

and associated shenanigans taking

place in London and across the UK to

mark the occasion. Here are ten ways

to join in without spending any money.



Stop-words removal










X X X X X X

X X X X

X X X X

X

X

X

X X X X X X

X X X X



Stop-words removal










X X X X X X

X X X X

X X X X

X

X

X

X X X X X X

X X X X

Shell thousands, survive various

ballots, swap family member ticket

enjoy 2012 Summer Olympic Games

year. Manner free events associated

shenanigans taking place London

across UK mark occasion. ten ways

join spending money.



Stemming










Stemming








X X X

X

X X X X

X X

X

X



Stemming








X X X

X

X X X X

X X

X

X Shell thousand, surviv various ballot,

swap famil member ticket enjoy 2012

Summer Olymp Game year. Manner

free event associat shenanigan tak

place London across UK mark

occasion. ten way join spend money.



Vector representation

TFIDF

<free0.0116>

<olymp, 0.0235>

<event, 0.0012>

<way, 0.0125>

<london, 0.0421>

<celebrat, 0.0005>

<chef, 0.0127>

…


IS ENOUGH THE SOLE SYNTACTIC APPROACH?



Polysemy…


“BASS”


Synonymity…


Vehicle

Car

Automobile

Auto

Machine

SEMANTIC TEXTUAL ANALYSIS

Taxonomy-based Classification

Word Disambiguation



Taxonomy-based Classification

Classification Features (CF) representation

Adopted classifiers

Rocchio

SVM



Rocchio

Each centroid is defined as a sum of TF-IDF values of each

term, normalized by the number of webpages in the class

The classification is based on the

cosine of the angle between the

webpage and the centroid of each class



SVM

The score is related to the

distance of the webpage from a

separation hyperplane



Word Disambiguation Bag of Concepts (BoC) representation

Adopted lexical supports WordNet

YAGO

ConceptNet



WordNet

A large lexical database

of English. Nouns, verbs,

adjectives and adverbs

are grouped into sets of

cognitive synonyms

(synsets), each

expressing a distinct

concept.



YAGO

A semantic knowledge base, derived from Wikipedia,

WordNet and GeoNames



ConceptNet

A network of concepts connected by several semantic

relations (e.g., “IsA”, “PartOf”)


MATCHING

Similarity calculation

Ranking


MATCHING

Similarity calculation

Adopted approaches

Cosine similarity

Jaccard index


MATCHING

o Cosine similarity


MATCHING

o Jaccard index

The Jaccard coefficient measures similarity between sample sets,

and is defined as the size of the intersection divided by the size of

the union of the sample sets


MATCHING

Ranking

Adopted approaches

Simple ranking according to the calculated scores

Learning to rank model


MATCHING

o Learning to rank model

Pointwise approach

o Each query-document pair in the training data has a numerical

or ordinal score

o Regression problem approach: given a single query-document

pair, predict its score

Pairwise approach

o Classification problem approach: learning a binary classifier

which can tell which document is better in a given pair of

documents

Listwise approach

o Optimization problem approach: try to directly optimize the

value of one of the above evaluation measures, averaged over

all queries in the training data


AN EXAMPLE: CONCA

CONCEPTS IN CONTEXTUAL ADVERTISING

CONCA


RESULTS

SYNTAX VS SEMANTICS

SYNTACTICAL ANALYSIS

Text summarization techniques comparison

FLP vs TFLP vs S vs TS

Comparison metrics

Taxonomy

BankSearch Dataset


FPTP

TP

documentsretrieved

documentsretrieveddocumentsrelevant

|}{|

|}{}{|

FNTP

TP

documentsrelevant

documentsretrieveddocumentsrelevant

|}{|

|}{}{|

21F

SYNTACTICAL ANALYSIS

Results

Adding information about the title improves the

performances

TFLP has the best performance


FLP TFLP S TS

π 0.745 0.832 0.734 0.806

ρ 0.719 0.801 0.730 0.804

F1 0.732 0.816 0.732 0.805

#t 24 26 12 14

SEMANTIC ANALYSIS

Semantic approaches comparison

Anagnostopoulos et al. (2007) system vs Armano et al.

(2011-TIR) vs ConCA

Matching function

Comparison metric


CFBoC simsimap )1(),(

N

i

k

j

ijij

N

i

k

j

ij

FPTP

TP

k

1 1

1 1

)(

@

SEMANTIC ANALYSIS

Ad repository

Built by hand by a domain expert

Taxonomy

BankSearch Dataset


SEMANTIC ANALYSIS

Results


k Anagnostopoulos

et al.

Armano et al. ConCA

π α π α π α

1 0.674 0 0.768 0.2 0.773 0.1

2 0.653 0 0.750 0.2 0.752 0.1

3 0.617 0.2 0.729 0.3 0.728 0.1

4 0.582 0.2 0.701 0.3 0.701 0.1

5 0.546 0.1 0.663 0.0 0.668 0.1

SEMANTIC ANALYSIS

Results


SEMANTIC ANALYSIS

Results

Slight improvement by using concepts

Low values of α → CF more impact then BoC


SYNTACTICAL ANALYSIS VS SEMANTIC ANALYSIS

Contextual Advertising System

Armano et al. (2011-TIR)

Matching function

Comparisons varying α

α = 1 → pure syntax

α = 0 → pure semantics

Comparison metric


CFBoW simsimap )1(),(

N

i

k

j

ijij

N

i

k

j

ij

FPTP

TP

k

1 1

1 1

)(

@


Ad repository

Built by hand by a domain expert

Taxonomy

BankSearch Dataset



Results


α π@1 π@2 π@3 π@4 π@5

0 0.765 0.746 0.719 0.696 0.663

0.1 0.767 0.749 0.724 0.698 0.663

0.2 0.768 0.750 0.729 0.699 0.662

0.3 0.766 0.749 0.729 0.701 0.661

0.4 0.756 0.747 0.729 0.698 0.658

0.5 0.744 0.735 0.721 0.693 0.651

0.6 0.722 0.717 0.703 0.681 0.640

0.7 0.685 0.687 0.680 0.658 0.625

0,8 0.632 0.637 0.635 0.614 0.586

0.9 0.557 0.552 0.548 0.534 0.512

1 0.408 0.421 0.372 0.388 0.640

CONCLUSIONS

CONCLUSIONS

Online advertising

represents one of the major sources of income for a large

number of websites

is aimed at suggesting products and services to the

population of Internet users

Modern contextual advertising systems

put ads within the content of a generic, third party,

webpage

adopt both syntactical and semantic textual analyses to

select the most relevant ads for a given webpage

an example is ConCA


CONCLUSIONS

Results show that

the impact of semantics is stronger than that of syntax

adopting more advanced semantic techniques, such as

concepts, improves the performances

the more the suggested ads are, the worse the

performance is


REFERENCES

REFERENCES

Syntactical Textual Analysis Armano G., Giuliani A., & Vargiu E. Experimenting text summarization

techniques for contextual advertising. 2nd Italian Information Retrieval

Workshop (IIR’11) , 2011.

Armano G., Giuliani A. & Vargiu, E. Using snippets in text summarization: a

comparative study and an application. 3rd Italian Information Retrieval

Workshop (IIR’12), 2012.

Kolcz A., Prabakarmurthi V. & Kalita J. Summarization as feature selection for

text categorization. 10th International Conference on Information and

Knowledge Management (CIKM’01). ACM, New York, NY, USA, pp. 365–370,

2001.

Porter M. An algorithm for suffix stripping. Program 14, 3, 130–137, 1980.

Salton G., Wong A. & Yang C.S, A vector space model for automatic indexing,

Communications of the ACM, 18, 11, pp.613-620, 1975.


REFERENCES

Semantic Textual Analysis Cortes C. & Vapnik, V.N. Support-Vector Networks, Machine Learning, 20,

1995.

Fellbaum C. WordNet: An Electronic Lexical Database. Cambridge, MA: MIT

Press, 1998.

Liu H. & Singh P. ConceptNet: A practical commonsense reasoning tool-kit. BT

Technology Journal 22, pp. 211–226, 2004.

Miller G.A. WordNet: A Lexical Database for English. Communications of the

ACM, 38, 11, pp. 39-41, 1995.

Rocchio J. The SMART Retrieval System: Experiments in Automatic Document

Processing. PrenticeHall, Chapter: Relevance feedback in information

retrieval, pp. 313–323, 1971.

Suchanek F.M., Kasneci G. & Weikum G. Yago - A Core of Semantic

Knowledge. 16th International World Wide Web conference (WWW 2007),

2007.


REFERENCES

Matching Liu T.Y. Learning to rank for information retrieval. Found. Trends Inf. Retr. 3, 3,

pp. 225–331, 2009.

Radomski P.J. & Goeman, T.J. The homogenizing of Minnesota lake fish

assemblages. Fisheries, 20, pp. 20–23, 1995.

Comparison Systems Anagnostopoulos A., Broder A. Z., Gabrilovich E., Josifovski V. & Riedel L. Just-

in-time contextual advertising. 16th ACM Conference on Information and

Knowledge Management (CIKM’07). ACM, New York, NY, USA, pp. 331–340,

2007.

Armano G., Giuliani A. & Vargiu E. Studying the impact of text summarization

on contextual advertising. 8th International Workshop on Text-based

Information Retrieval (TIR’11), 2011.

Armano G., Giuliani A. & Vargiu E. Semantic enrichment of contextual

advertising by using concepts. International Conference on Knowledge

Discovery and Information Retrieval, 2011.


Contact: Eloisa Vargiu – [email protected]

Textual Analysis Techniques to Perform Onlibne Advertising

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