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

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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

Ignacio Fernández-Tobías

under the supervision of

Iván Cantador

Grupo de Recuperación de Información

Universidad Autónoma de Madrid

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1

• 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

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2 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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3 Introduction: Cross-domain item recommendation

• 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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4 Introduction: Cross-domain item recommendation

• 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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5 Introduction: Cross-domain item recommendation

• 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

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6

• 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

(8)

7

• Case study

: Suggesting music / musicians highly related to a particular

point of interest (POI)

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8

• Case study

: Suggesting music / musicians highly related to a particular

point of interest (POI)

• Relations between music and places

‐

Based on common

emotions

caused by listening to music and visiting

POIs



social tags

Case study: Linking music with places of interest

Kaminskas, M., Ricci, F. 2011. Location-Adapted Music Recommendation Using Tags.

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9

• Case study

: Suggesting music / musicians highly related to a particular

point of interest (POI)

• 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

Case study: Linking music with places of interest

Vienna State Opera

Gustav Mahler

Wolfgang Amadeus Mozart

Arnold Schoenberg

Classical music

Austrian musicians

Opera composers

19th century

Romanticism

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10

• 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 “

” 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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11

• 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

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12 Cross-domain semantic networks from Wikipedia

City

Building type

(Architecture) categories

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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

Cross-domain semantic networks from Wikipedia

• 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

.

(15)

14 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

Cross-domain semantic networks from Wikipedia

• 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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15 Cross-domain semantic networks from Wikipedia

• 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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16 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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17 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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18 Cross-domain semantic networks from Wikipedia

• 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

H

i

j

𝑠𝑐𝑜𝑟𝑒 𝑖 ← 𝑆 𝑖 = 1 − 𝑑 · rel 𝑖 + 𝑑 · 𝑤

_𝑗𝑖

𝑆(𝑗)

𝑗→𝑖

j

i

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19 Cross-domain semantic networks from Wikipedia

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20 Cross-domain semantic networks from Wikipedia

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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21 Cross-domain semantic networks from Wikipedia

Average number of semantic paths per POI

Interesting

Non interesting

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

.

(23)

22

• 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

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23 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.

Linked to Freebase

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24 Fernández-Tobías, I., Cantador, I. 2013. Open Cross-domain Semantic Networks:

Application to Item-to-item Recommendation.

To be submitted

.

Cross-domain semantic networks from Open Information Extraction

• Filtering

relations

based on a TF-IDF heuristic

𝑤 𝑒

₁

, 𝑟, 𝑒

₂

= 𝜆

𝑐 𝑒

1 , 𝑒

2 𝑐 𝑒

_𝑖

, 𝑒

_𝑗

𝑒

_𝑖

,𝑒

_𝑗

+ 1 − 𝜆 tfidf(𝑟)

tfidf 𝑟 =

𝑒

𝑖

, 𝑟, 𝑒

𝑗

∈ 𝐺

max

𝑠

𝑒

𝑖

, 𝑠, 𝑒

𝑗

· log

𝑁

𝑒

_𝑖

, 𝑟, 𝑒

_𝑗

∈ 𝒞

• Ranking

entities

according to node categories and graph structure

𝑤 𝑒 = 𝛼

₁

𝑤

_𝑇

𝑒 + 𝛼

₂

𝑤

_𝑃

(𝑒) + 𝛼

₃

𝑤

_𝐷

(𝑒)

𝑤

_𝑇

𝑒 = 𝑇 𝑒 ∩ 𝐷 ·

𝑇 𝑒 ∩ 𝐷

𝑇(𝑒)

𝑤

_𝑃

𝑒 = 𝑠 → 𝑒

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25

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26

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27

• 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

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

• Mining social tagging systems to create linked emotion-oriented

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29

• 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

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

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, ….

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

• Generic emotion lexicon

• In accordance with

Russell’s emotion model

(1980)

‐

Emotion representation in 2 dimensions: pleasure & arousal

AROUSAL SLEEPINESS PLEASURE MISERY DISTRESS EXCITEMENT CONTENTMENT DEPRESSION 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 0.15

Russell, J. A. 1980. A Circumplex Model of Affect.

Journal of Personality and Social Psychology

39(6), pp. 1161-1178.

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

• 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)

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