Enhanced Semantic TV-Shows Representation for Personalized Electronic Program Guides

UMAP 2012 - Industrial Track
Montréal (Canada), 19.07.2012

Cataldo Musto, Fedelucio Narducci, Pasquale Lops, Giovanni Semeraro, Marco de Gemmis (University of Bari, Aldo Moro)
Mauro Barbieri, Jan Korst,Verus Pronk and Ramon Clout (Philips Research, Eindhoven, The Netherlands)

Enhanced Semantic TV-Show Representation
for Personalized Electronic Program Guides

exponential growth
of available TV assets
C. Musto, F. Narducci, G. Semeraro, P. Lops, M. de Gemmis, M. Barbieri, J. Korst,V. Pronk, R. Clout
Enhanced Semantic TV-Shows Representation for Personalized Electronic Program Guides - UMAP 2012 - 19.07.12

Some stats
4 hours watched every day
out of 3000 hours of broadcast TV shows

ratio

0.013%
source: Nielsen Survey, 2011


Information Overload


what TV shows should I watch?

industrial scenario

how does Philips cope with the
overload of TV shows?


solution

personalization.

recommender systems


content-based recommenders
key concepts

• Each item (TV show) has to be described through a set
of features
• Description of TV shows, plot of the movie and so on.
• Each user is described through the features that occur
in TV shows she watched (liked) in the past

• Recommendations are provided by calculating the
overlap between the textual description of the TV
show and the features stored in the user proﬁle

example: TV shows recommendations
user proﬁle recommendations

♥
basketball nba (basketball)

♥

football documentary


example: TV shows recommendations
user proﬁle recommendations

♥
basketball nba (basketball)

X
♥

football documentary


personal channels
‘in vitro’ experiments
concept

Idea: combining boolean ﬁlters to ﬁlter TV shows
and recommenders to rank them.

Watchmi plug-in
developed by Aprico.tv
‘in vitro’ experiments


problem
descriptions of TV shows are often
too short or poorly meaningful
to feed a content-based
recommendation algorithm


solution
feature generation techniques
based on open knowledge sources


explicit semantic analysis
• Explicit Semantic Analysis (ESA) (Gabrilovitch and Markovitch, 2006)

• Goals To introduce a methodology for representing the knowledge
stored in Wikipedia

• To deﬁne a relationship between terms in natural language and
Wikipedia articles

• Insights

• ESA provides a vector-space representation for each term

• Terms are represented as rows in a matrix (called ESA matrix) where
each column is a Wikipedia concept (article)

ESA representation
term/document matrix

a1 a2 a3 a4 a5 a6 a7 a8 a9

t1 ✔ ✔ ✔ ✔

t2 ✔ ✔ ✔ ✔

t3 ✔ ✔ ✔

t4 ✔ ✔ ✔ ✔

ESA representation
term/Wikipedia articles matrix

a1 a2 MotoGP a4 a5 a6 a7 a8 a9

t1 ✔ ✔ ✔ ✔

t2 ✔ ✔ ✔ ✔

t3 ✔ ✔ ✔

t4 ✔ ✔ ✔ ✔

ESA representation
MotoGp

Cat$[0.92]+
Superbike (0.92)

Every Wikipedia article is a concept Leopard$[0.84]+
grand prix (0.76)
Each concept is represented through the
TF-IDF scores of the terms that occur in the valentino rossi
Roar$[0.77]+
article (0.59)

ESA representation
term/Wikipedia Articles matrix

Politics MotoGP Basketball M.Biaggi V.Rossi

Superbike ✔ ✔ ✔

t2 ✔

t3 ✔

t4 ✔ ✔ ✔

ESA representation
Each term can be deﬁned upon the
Wikipedia concepts it occurs in
MotoGP Max Biaggi Jane.
Bridgestone
Superbike
Cat$ Cat$ Panthera(
Fonda$
[0.95]$
(0.92) (0.63)
[0.92]( (0.43)
[0.07]$

“ the semantics of a term is the vector of its associations with Wikipedia articles”

the whole vector is called
Semantic Interpretation Vector

ESA representation
semantics of text fragments

Mouse+ Mouse+ Mickey% John+
mouse& rodent* computing* Mouse% Steinbeck&
[0.91]& [0.89]& [0.81]% [0.17]&

Dick+ Mouse+ Game%
Bu#on&
bu#on& [0.93]&
Bu#on& computing* Controller%
[0.84]& [0.81]& [0.32]%

DragB+
mouse++ Mouse+ Mouse+ IBM&
andB
computing* rodent* PS/2*
bu#on& [0.85]& [0.46]& [0.35]&
drop&
[0.32]&

calculated as the centroid vector of the semantic
interpretations vectors that compose the fragment

ESA has already been adopted for text
classiﬁcation, information retrieval and
semantic relatedness computation

Research Question
How can we exploit ESA for performing
feature generation in the scenario of EPGs
personalization?


From BOW to eBOW

Given a description of a TV show, we exploit ESA to
obtain an enhanced representation

The original set of features is enriched with the set of
Wikipedia articles related the most with the TV show


From BOW to eBOW
algorithm

Concept$ Concept$ Concept$ Concept$
centroid BOW$ 1! 47! 50! n$
vector [0.85]$ [0.46]$ [0.35]$ [0.32]$

The centroid vector of the whole description of the TV
show is calculated
The n most related Wikipedia concepts are extracted
Concepts are added to the original BOW to
obtain an enhanced BOW (e-BOW)


From BOW to eBOW
example
Wikipedia(Articles(
großer&preis&von&italien&
(motorrad)&
großer&preis&von&malaysia&
(motorrad)&
großer&preis&von&tschechien&
(motorrad)&
scuderia&ferrari&
valen8no&rossi&
motorrad9wm9saison&2005&
motorrad9wm9saison&2006&
max&biaggi&
TV SHOW großer&preis&der&usa&(motorrad)&
Rad an Rad motorrad9wm9saison&2008&
Die besten Duelle der MotoGP rad&(heraldik)&
(Wheel to wheel loris&capirossi&
The best duels in the MotoGP) shin’ya&nakano&
motogp&


what about the
advantages?


example
BOW representation
user proﬁle tv show

motogp
sports 2012 Superbike
motorbike Italian Grand
Prix
...
competition

example
BOW representation

X

motogp
sports 2012 Superbike
No matching! Italian Grand
motorbike
Prix
...
competition

example
eBOW representation

motogp
superbike
2012 Superbike
sports
motorbike
Italian Grand
formula 1 Prix
...
competition


example
eBOW representation

✔

motogp
superbike
2012 Superbike
sports
Matching! Italian Grand
motorbike
formula 1 Prix
...
competition


ESA advantages

knowledge is ﬂuid.


ESA advantages

knowledge is ﬂuid.
it is necessary to exploit open and
always updated knowledge sources


example
concept: ‘American Politics’
Year Enrichment
2000 Clinton

2005 Bush

2011 Obama


(counter)example
concept: ‘Italian Politics’
Year Enrichment
2000 Berlusconi

2005 Berlusconi

2011 Berlusconi


experiments.


design of the experiments
task

• retrieval task
• Given a set of program types and a repository of TV
shows

• We want to retrieve the shows that belong to a
speciﬁc program type

Movie


dataset
Aprico.tv data
• Dataset
• 47 German-language Channels provided by Axel Springer

• 133k TV Shows, 17 program types

• Textual features: title, synopsis, description, program type

• Explicit Semantic Analysis
• Dump: October, 2010

• 814,013 terms (rows) and 484,218 articles (colums)


learning methods
• Two state-of-the-art learning methods have been compared

• Random Indexing

• Vector Space Model (VSM)-based representation

• Incremental approach to compress the representation in an effective
way

• Both TV shows and user profile are points in a vector space

• Logistic Regression

• Supervised Learning Method, state of the art for Text Classification

• Each TV show is classified as relevant or not relevant for the user,
according to user profile

• TV shows can be ranked according to their probability scores

research questions

1.
Which one is the learning method than can
provide the best recommendations ?

2. Does the idea of enriching the
BOWs with ESA improve the
accuracy of the suggestions ?


experiment 1
results
100
Logistic Regression
Random Indexing
87,5

75

62,5

50
P@5% P@10% P@25% P@50% P@75% P@100%


experiment 2
results
95
BOW
eBOW (+20)
eBOW (+40)
eBOW (+60)
89,75

84,5

79,25

74
P@5% P@10% P@25% P@50% P@75% P@100%


experiment 2
results
95
BOW
eBOW (+20)
eBOW (+40)
eBOW (+60)
89,75

84,5 Differences between BOW
and eBOW(+40, +60) are
statistically signiﬁcant
79,25 (Mann-Whitney Test,
p<0,005)
74
P@5% P@10% P@25% P@50% P@75% P@100%


Recap
• Content-based Personalization Techniques for
Electronic Program Guides
• Joint work: Philips Research - Aprico.tv - University of Bari

• Feature generation to enrich textual descriptions of TV shows
• Exploitation of ESA: Explicit Semantic Analysis
• Introducing eBOW for content representation
• BOW + Wikipedia concepts related to the textual description


Conclusions
• Linear Regression can provide good accuracy in retrieving
related TV shows
• Almost 90% in precision.
• Feature Generation techniques based on Wikipedia can
improve the precision of a content-based recommendation
approach
• eBOW representation overcomes the classical BOW
representation

• Good results: 94% in precision

questions?


Enhanced Semantic TV-Shows Representation for Personalized Electronic Program Guides

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