Exploiting the Web of Data for cross-domain information ...1 Exploiting the Web of Data for cross-domain information retrieval and recommendation VII Jornadas MAVIR - Avances en Tecnologías

Exploiting the Web of Data for cross-domain

information retrieval and recommendation

VII Jornadas MAVIR

Avances en Tecnologías de la Lengua y Acceso a la Información Multimedia

Escuela Politécnica Superior, Universidad Carlos III de Madrid

26-27 November 2012

Ignacio Fernández-Tobías

under the supervision of

Iván Cantador

Grupo de Recuperación de Información

Universidad Autónoma de Madrid

[email protected]

1

Exploiting the Web of Data for cross-domain information retrieval and recommendation

VII Jornadas MAVIR - Avances en Tecnologías de la Lengua y Acceso a la Información Multimedia

Madrid, 26-27 November 2012

• Introduction: Cross-domain item recommendation

• Case study: Linking music with places of interest

• A semantic-based framework for linking domains

• Cross-domain semantic networks from Wikipedia

• Cross-domain semantic networks from Open Information Extraction

• A social tag-based emotion-oriented approach for linking domains

Contents

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Introduction: Cross-domain item recommendation

• Recommender systems help users to make choices, by proactively

finding relevant items or services, taking into account or predicting the

users’ tastes, priorities and goals

• The vast majority of the currently available recommender systems predicts

the user’s relevance of items in a specific and limited domain

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• In some applications, it could be useful to offer the user joint personalized

recommendations of items belonging to multiple domains

• In an e-commerce site, we may suggest movies or videogames based on a

particular book bought by a costumer

• In a travel application, we may suggest cultural events may interest a person

who has booked a hotel in a particular place

• In an e-learning system, we may suggest educational websites with topics

related to a video documentary a student has seen

• Potential benefits

• Offering diversity and serendipity

• Addressing the cold-start problem (on a target domain)

• Mitigating the sparsity problem

Fernández-Tobías, I., Cantador, I., Kaminskas, M., Ricci, F. 2012. Cross-domain Recommender Systems: A Survey of the State of the Art. 2nd Spanish Conference on Information Retrieval.

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• Some real applications (e.g. Amazon) do already recommend items from

different domains, but

• their recommendations rely on statistical analysis of popular items, without any

personalization strategy, or

• most of them only exploit information about the user preferences in the target

domain

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• Context

• User and item profiles are distributed in multiple systems

there is no / a few user profiles with preferences on items in different domains

• Goal

• Automatically establishing links or transferring knowledge between domains

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Contents

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• Case study: Suggesting music / musicians highly related to a particular

point of interest (POI)

Case study: Linking music with places of interest

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• Relations between music and places

‐ Based on common emotions caused by listening to music and visiting

POIs social tags


Kaminskas, M., Ricci, F. 2011. Location-Adapted Music Recommendation Using Tags. 19th Intl. Conference on User Modeling, Adaptation and Personalization, 183-194.

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• Relations between music and places

‐ Based on common emotions caused by listening to music and visiting POIs

social tags

‐ Based on explicit semantic associations between musicians and POIs

information available in the (Semantic) Web


Vienna State Opera

Gustav Mahler

Wolfgang Amadeus Mozart

Arnold Schoenberg

Classical music

Austrian musicians

Opera composers

19th century

Romanticism

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• Semantic relations between musicians and POIs

• Location relations

‐ Arnold Schoenberg was born in Vienna, which is the city where Vienna State

Opera is located

• Time relations

‐ Gustav Mahler was born in 1869, which is a year in the decade when Vienna State

Opera was built

• Architecture-History/Art-Music “category” relations

‐ Wolfgang A. Mozart was a classical music composer, and classical compositions

are played in Opera houses, which is the building type of the Vienna State Opera

• Arbitrary relations

‐ Gustav Mahler was the director of Vienna State Opera

‐ Ana Belén (a famous Spanish singer) composed a song about La Puerta de Alcalá

(a well known POI in Madrid)


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Contents

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Cross-domain semantic networks from Wikipedia

City

Building type

(Architecture) categories

Date

13




Visitor

attractions

Arts

venues

Music

venues Opera

houses

Opera Classical

music

Music

genres

Opera

composers

Architectural

styles

19th century

architecture

19th century

Modern

history

Historical

eras

Music

people

Musicians

Composers

Romanticism

18th century

19th century

in music

19th century

musicians

Romantic

composers

Classical

composers

19th century

composers


• Linking Wikipedia’s architecture and music categories

Kaminskas, M., Fernández-Tobías, I., Ricci, F., Cantador, I. 2013. Ontology-based Identification of Music for Places. 13th Intl. Conference on Information and Communication Technologies in Tourism.

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Visitor

attractions

Arts

venues

Music

venues Opera

houses

Opera Classical

music

Music

genres

Opera

composers

Architectural

styles

19th century

architecture

19th century

Modern

history

Historical

eras

Music

people

Musicians

Composers

Romanticism

18th century

19th century

in music

19th century

musicians

Romantic

composers

Classical

composers

19th century

composers


• Linking Wikipedia’s architecture and music categories

Kaminskas, M., Fernández-Tobías, I., Ricci, F., Cantador, I. 2013. Ontology-based Identification of Music for Places. 13th Intl. Conference on Information and Communication Technologies in Tourism.

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• Cross-domain taxonomies from Wikipedia

• Architecture

• History / Art

• Music

Visitor

attractions

Arts

venues

Music

venues

Opera

houses

Historical

eras

Modern

history

Romanticism

19th century

18th century

Centuries

Architectural

styles

Centuries in

architecture

19th century

architecture

Music

genres

Classical

music

Opera

Music

people

Romantic composers

Classical composers

Composers

Musicians

Opera composers

19th century musicians

19th century composers

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POI

City

Date

Year

Decade

Century

Architectural

style

Musician

type

Musician

located_in

has_style

genre_of

type_of

birth_place_of

death_place_of

residence_place_of

birth_date_of

death_date_of

activity_date_of

Music

genre

Building

type

Musical

era

Historical

era

Architectural

era has_type

subcategory_of

building_start_date_of

building_end_date_of

opening_date_of

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Vienna,

Austria

1869

Opera

houses in

Austria

19th

century

architecture

Gustav

Mahler

19th

century

Opera

houses

Opera

houses in

Vienna

1869

architecture

Opera

Romanticism

19th

century

Vienna

State

Opera

Romantic

music Architectural

styles

Building types Music genres

Musician

types

Architectural

eras Historical

eras Musical

eras

Date

City

birth_decade_of

activity_century_of

death_place_of

19th

century in

music

1860s

19th

century

composers

Classical

music

Romantic

composers

Classical

composers

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• Weight Spreading Activation

• PageRank

• HITS

𝑠𝑐𝑜𝑟𝑒 𝑖 ← 𝑃𝑅 𝑖 = 1 − 𝑑 ·1

𝑁+ 𝑑 ·

1

𝐿(𝑗)𝑗→𝑖

𝑃𝑅(𝑗)

𝑠𝑐𝑜𝑟𝑒 𝑖 ← 𝐴 𝑖

𝐴 𝑖 = 𝐻(𝑗)

𝑗→𝑖

𝐻 𝑖 = 𝐴(𝑗)

𝑖→𝑗

H

A

A

H

i

j

𝑠𝑐𝑜𝑟𝑒 𝑖 ← 𝑆 𝑖 = 1 − 𝑑 · rel 𝑖 + 𝑑 · 𝑤𝑗𝑖𝑆(𝑗)

𝑗→𝑖

j i

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• 97 users, 17 cities, 25 POIs, 356 POI-musician pairs, 1155 assessments

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Average precision values for the top 5 ranked musicians for each POI

P@1 P@2 P@3 P@4 P@5

Random 0.355* 0.391* 0.363* 0.435* 0.413*

HITS 0.688 0.706 0.711* 0.700* 0.694

PageRank 0.753 0.728 0.707* 0.660* 0.646*

Spreading 0.810 0.804 0.828 0.847 0.837

The values marked with * have differences statistically significant with Spreading algorithm’s

(Wilcoxon signed-rank test, p<0.05)

Fernández-Tobías, I., Kaminskas, M., Cantador, I., Ricci, F. 2013. A semantic framework for supporting cross-domain recommendation: Suggesting music for places of interest. Submitted.

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Average number of semantic paths per POI

Interesting Non interesting

Related 78.3% 21.7%

Non-related 8.2% 91.8%

Percentages of interesting and obvious musicians recommended by

Spreading algorithm Non obvious Obvious

58.9% 41.1%

84.2% 15.8%

Fernández-Tobías, I., Kaminskas, M., Cantador, I., Ricci, F. 2013. A semantic framework for supporting cross-domain recommendation: Suggesting music for places of interest. Submitted.

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Contents

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Cross-domain semantic networks from Open Information Extraction

• TextRunner (openie.cs.washington.edu) and ReVerb (reverb.cs.washington.edu):

• Automatically identification and extraction of binary relationships from English

sentences

Etzioni, O., Fader, A., Christensen, J., Soderland, S., Mausam. 2011. Open Information Extraction: The Second Generation. 22nd International Joint Conference on Artificial Intelligence, pp. 3-10.

Linked to Freebase

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Fernández-Tobías, I., Cantador, I. 2013. Open Cross-domain Semantic Networks: Application to Item-to-item Recommendation. To be submitted.


• Filtering relations based on a TF-IDF heuristic

𝑤 𝑒1, 𝑟, 𝑒2 = 𝜆𝑐 𝑒1, 𝑒2

𝑐 𝑒𝑖 , 𝑒𝑗𝑒𝑖,𝑒𝑗

+ 1 − 𝜆 tfidf(𝑟)

tfidf 𝑟 =𝑒𝑖 , 𝑟, 𝑒𝑗 ∈ 𝐺

max𝑠𝑒𝑖 , 𝑠, 𝑒𝑗

· log𝑁

𝑒𝑖 , 𝑟, 𝑒𝑗 ∈ 𝒞

• Ranking entities according to node categories and graph structure

𝑤 𝑒 = 𝛼1𝑤𝑇 𝑒 + 𝛼2𝑤𝑃(𝑒) + 𝛼3𝑤𝐷(𝑒)

𝑤𝑇 𝑒 = 𝑇 𝑒 ∩ 𝐷 ·𝑇 𝑒 ∩ 𝐷

𝑇(𝑒)

𝑤𝑃 𝑒 = 𝑠 → 𝑒

𝑤𝐷 𝑒 = dist(𝑠, 𝑒)

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Contents

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A social tag-based emotion-oriented approach for linking domains

• Mining social tagging systems to create linked emotion-oriented

folksonomies

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• Generic emotion lexicon

• Automatically created by mining online thesauri (e.g. thesaurus.com)

• 16 main emotions: alert, excited, elated, happy, content, serene, relaxed,

calm, fatigued, bored, depressed, sad, upset, stressed, nervous, tense

• Emotion = synonym & antonym vector

‐ Synonyms: positive weights

‐ Antonyms: negative weights


Fernández-Tobías, I., Plaza, L., Cantador, I. 2013. Cross-domain Emotion Folksonomies. To be submitted.

happy:+66, cheerful:+ 21, merry:+19, felicitous:+17, …

unhappy:–11, sad:–10, depressed:–6, serious:–4, ….

happy

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• Generic emotion lexicon

• In accordance with Russell’s emotion model (1980)

‐ Emotion representation in 2 dimensions: pleasure & arousal

AROUSAL

SLEEPINESS

PLEASUREMISERY

DISTRESS EXCITEMENT

CONTENTMENTDEPRESSION

excited

alert

happy

elated

content

relaxed

calm

serene

bored

fatigued

depressed

sad

stressed

upset

nervous

tense

alert

excited

elated

happy

content

serene

relaxed

calm

fatigued

bored

depressed

sad

upset

stressed

nervous

tense

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15Russell, J. A. 1980. A Circumplex Model of Affect. Journal of Personality and Social Psychology 39(6), pp. 1161-1178.

Russell’s emotion model Obtained emotion vectors projected into 2 dimensions (PCA)

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• Domain-dependent emotion folksonomies

• Particular emotional categories in each domain

• Each category is composed of a set of concurrent tags in the domain

folksonomy

• Movies (MovieLens, Jinni, IMDb)

• bittersweet, emotional, feel good, scary, …

• Music (Last.fm, GEMS)

• wonder, tenderness, nostalgia, peacefulness, …

• Books (BookCrossing, LibraryThing, Whichbook)

• funny, unpredictable, disgusting, violent, …

Exploiting the Web of Data for cross-domain

information retrieval and recommendation

VII Jornadas MAVIR

Avances en Tecnologías de la Lengua y Acceso a la Información Multimedia

Escuela Politécnica Superior, Universidad Carlos III de Madrid

26-27 November 2012

Ignacio Fernández-Tobías

under the supervision of

Iván Cantador

Grupo de Recuperación de Información

Universidad Autónoma de Madrid

[email protected]

33





• Vienna State Opera Arnold Schoenberg

• Arnold Schoenberg was born in Vienna, where Vienna State Opera is located

• Arnold Schoenberg was born in the 19th century, when Vienna State Opera was built

• Arnold Schoenberg was a Classical music composer, Classical music genre is related

to Opera houses, which is the building type of Vienna State Opera

• Las Ventas Antonio Flores

• Antonio Flores was born in Madrid, where Las Ventas is located

• Antonio Flores died in the 20th century, when Las Ventas was built

• Antonio Flores was a Flamenco singer, Flamenco is a Romanic music genre and is

related to Moorish architecture, and Moorish Revival architecture is the architectonical

style of Las Ventas

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Average precision values obtained by the Spreading algorithm for the top

5 ranked musicians for each POI type

P@1 P@2 P@3 P@4 P@5

Music venues (4)

0.838 0.688 0.838 0.829 0.870

Religious buildings (8)

0.721 0.965 0.844 0.795 0.781

Castles and palaces (6)

0.794 0.704 0.792 0.900 0.825

Other POIs (7)

0.908 0.772 0.836 0.872 0.893

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• Evaluating if tracks of the retrieved musicians are relevant to POIs

Documents

Exploiting the Web of Data for cross-domain information ...1 Exploiting the Web of Data for cross-domain information retrieval and recommendation VII Jornadas MAVIR - Avances en Tecnologías