Context-Aware Recommendation

Context-Aware Recommendation

Intelligent Database Systems LabSchool of Computer Science & Engineering

Seoul National University, Seoul, Korea

Dongjoo Lee

Center for E-Business TechnologySeoul National UniversitySeoul, Korea

Copyright 2008 by CEBT

Introduction

Traditional recommendation methods

Content-based recommendation

– What’s the features that can describe the item?

Collaborative filtering

– Item based CF

– User based CF

– Hybrid CF

Issues in using context information in recommendation

1) What is context information?

2) How to use context information?

3) Is it really useful to use context information in recommen-dation?

2

0.9 0.3 0.20.8 0.50.7 0.6 0.7

0.6 0.70.4 0.3 0.40.1 0.8 0.7 0.5

item

user


MusicRecommender

Recommendation

3

(User1 hasSimilarTasteWithf User2) (User2 likesf Song2) (User1 notListened Song2)=> Recommendf Song2 to User1

U1

U2

(User1 likesf Song1) (Song2 isSimilarWithf Song1)(User1 notListened Song2)=> Recommendf Song2 to User1

Collaborative Filtering Content based Recommendation

m1

Recommendf hasSimilarTasteWithf isSimilarWithf notListened likesf

m3

Listen (when, where,…)

Vocabularies

m2

m5

m6

m7

m4

HasFeatureValue

Interpreter

Recommend

Assumption.If user1 scored similarly with user 2User1 and user 2 has similar taste.

Assumption.If music1 and music2 have similar feature valuesMusic1 and music2 is similar.

Interpreter

Logs

RulesInput: User1, User DB, Song DB, User Listen Log

Output: Sorted Song List Output: Sorted Song List

Assumption.If there is not listen log, user didn’t listen a song before.

Assumption.If user listen a song frequently, user likes it.

Interpreter

Interpreter

Interpreter

Input: User1, User DB, Song DB, User Listen Log


Recommendation (cont’d)

Recommendation

Context-Aware Recommendation

1. Context-Aware Collaborative Filtering

2. Context-Aware Contents-based Recommendation

1) Context abstraction

2) Context grouping

3) Item abstraction

4) Item grouping

5) User profiling

4

)|()( umPmscore like

),|()( ucmPmscore like

Copyright 2008 by CEBT 5

0.2 0.2

0.8 0.5

0.7 0.6 0.7

0.6 0.7

0.4 0.3 0.4

0.1 0.8 0.7 0.5

1. Context-Aware Collaborative Filtering

item

user

context

0.3 0.1 0.9

0.8 0.5

0.7 0.6 0.3

0.6 0.7

0.4 0.3 0.4

0.1 0.8 0.7 0.5

0.9 0.7 0.2

0.8 0.5

0.7 0.6 0.6

0.6 0.7

0.4 0.3 0.4

0.1 0.8 0.7 0.5

0.7 0.2 0.1

0.8 0.5

0.7 0.6 0.1

0.6 0.7

0.4 0.3 0.4

0.1 0.8 0.7 0.5

There are too many probable contexts

각 상황 별로 사용자의 성향을 구분하고 , 상황 별로 Collaborative Filtering 을 수행한다 . Active context ( 추천해야 하는 현재 상황 ) 에 해당하는 상황을 찾고 이에 따라서 추천을 수행한다 .


2. Context-Aware Content-based Recom-mendation

6

USER_ID ARTIST TITLE TIME LOCATION2monkeyflower Ada Our Love Never Dies 2008 04 4 05:05:002monkeyflower Ada Our Love Never Dies 2008 05 23 07:59:002monkeyflower Adam Green Hard to Be a Girl 2007 07 3 00:00:002monkeyflower Adam Green Salty Candy 2007 07 5 00:00:002monkeyflower Adriana Calcanhoto Segundos 2008 10 20 15:37:002monkeyflower Aerospace December Slow 2007 10 29 00:00:002monkeyflower Aesop Rock Basic Cable 2007 07 5 00:00:002monkeyflower Afterlife (feat. Neve) Elijah 2008 11 10 07:13:002monkeyflower Afterlife (feat. Neve) Elijah 2008 11 14 08:20:002monkeyflower Afterlife (feat. Neve) Elijah 2008 11 7 05:51:002monkeyflower Aim Linctus 2008 07 24 05:47:002monkeyflower Air Alone in Kyoto 2007 04 12 00:00:002monkeyflower Air Cherry Blossom Girl 2007 07 12 00:00:002monkeyflower Air Dead Bodies 2007 07 18 00:00:002monkeyflower Air Traffic Shooting Star (Demo) 2008 05 26 01:57:002monkeyflower Aitch Beautiful Girl 2008 07 17 06:18:002monkeyflower Akron/Family Future Myth 2008 01 31 08:37:002monkeyflower Akron/Family Gone Beyond 2007 05 28 00:00:002monkeyflower Akron/Family Love And Space 2007 06 20 00:00:00

…

Last.fm user listen logs

ARTIST TITLEAda Our Love Never DiesAdam Green Salty CandyAdriana Calcanhoto SegundosAerospace December SlowAfterlife (feat. Neve) ElijahAim LinctusAir Alone in KyotoAirbase GenieAitch Beautiful GirlAkron/Family Future MythAkron/Family Love And SpaceDavid Bowie Come And Buy My ToysDiana Ross (I Love) Being in Love With YouDuran Duran All She Wants IsEndre Kallocain (Robert Nickson Remix)Frank Sinatra The Way You Look TonightJose Amnesia Hentai (dj tom-x mix)Kyau vs. Albert Made Of Sun [KvA Volume Three Mix]Liberty X Never Give UpMidway IncaNorah Jones Humble MeNu NRG Le MirageQueen Don't Try So HardR.E.M. Walk UnafraidRachel Stevens Sweet Dreams My L.A. Ex (Bimbo Jones club mix)Robbie Williams Mack the KnifeSalt Tank Sargasso SeaSolar Factor Urban Shakedown (original mix)Sunny Lax M.I.R.A.The Cure TrapVDM ShamuWet Wet Wet Morning (96 Remix)Whitney Houston Heartbreak Hotel

…

Last.fm music

Context Group 1

Music Group 2These songs can be recommended.

Context Group 2

0.9

0.3Music Group 1


2. Context-Aware Content-based Recom-mendation

7

mg1

User

ItemContext

cg2

cg1

cgm

ContextGroup

mg2

mg2

… … …

ItemGroup

MGmg CGcg

MGmg

like

ccgPucgmgPmgmP

ucmgPmgmP

ucmPmscore

)|(),|()|(

),|()|(

),|()(

c

Active context

1

32

132

Model based recommendation


Abstraction in Music Domain

8

User Song

Tag

listen

timetagged

AlbumArtist

songBy

trackOf

Context Concept

belongs

tagged

tagged

Context AbstractionSemantic Annotation = Abstraction with domain concepts

Context can be obtained from users’ listen logs

location occasion

Learn user’s preference from

listen logs

name

id

gender

Tag_count

Track_count

age

country

count

count

count

User Time Loca-tion

Mu-sic

A … … M1

…

Users listen logs


1) Context Abstraction

Filter

sensed data

filtered data

concept

context

context

context

context

Coolcontext

Filter

Sensor

Sensor

Context Concepts

fuzzy membership function

filtered data


1) Context Abstraction – Fuzzy Join

10

Concept

Hot

Cool

Cold

…

Temp.

39

28

17

7

-1

-20

f

Temp.Con-cept

Fuzzi-ness

39 Hot 0.98

28 Hot 0.84

17 Hot 0.20

7 Hot 0

-1 Hot 0

-20 Hot 0

39 Cool 0

28 Cool 0.1

17 Cool 0.87

7 Cool 0.5

-1 Cool 0.05

-20 Cool 0

39 Cold 0

28 Cold 0

17 Cold 0

7 Cold 0.5

-1 Cold 0.87

-20 Cold 0.99

Temp.Con-cept

Fuzzi-ness

39 Hot 0.98

28 Hot 0.84

17 Hot 0.20

17 Cool 0.87

7 Cool 0.5

7 Cold 0.5

-1 Cold 0.87

-20 Cold 0.99

Temperature

Fuzzy Join Functions

HotCoolCold

Fuzz

iness

Context Data Abstract ContextConcepts

Product of two relation Fuzzy Join Result

α-cut may improve query performance


1) Context Abstraction – Fuzzy Equi-Join

Normal Equi-Join

Fuzzy Equi-Join

the most important thing is fuzzy function (≈) that com-pares two values

obtain fuzzy membership degree

Performance Improvement

– Sort-Merge Join using partial order of fuzzy similarity

11

SELECT T1.*, T2.*FROM table1 T1 JOIN table2 T2 ON T1.a = T2.b

SELECT T1.*, T2.*, FuzzyValueFROM table t1 JOIN table t2 ON t1.a ≈ t2.bWHERE FuzzyValue > THETA


1) Context Abstraction – Periodic Membership Function

Modified Cosine Function

Because temporal value is periodic , periodic function is appropriate for calculating membership degree to the temporal concepts.

time

f(x) = max(min(10.0 * cos( 2pi * (x - (150) ) / 1440 - 8.5), 1, 0)

dawn

midnight

f(x) = max(min(7.0 * cos( 2pi * (x - (60) ) / 1440 - 5.5), 1, 0)

f(x) = max(min(4.0 * cos( 2pi * (x - (172800) ) / 525600 - 2.4), 1, 0)

Spring

)))/2cos(,1min(,0max()( dcbxaxf

Dawn, Morning, Noon, Afternoon, Evening, Night, Mid-night

Monday, Tuesday, Wednesday, Thursday, Friday, Satur-day, Sunday

Spring, Summer, Autumn, Winter

New Year’s Day, Valentine’s Day, White Day, Children’s Day, Parents’ Day, Christmas


2) Context Grouping

1) Atomic Context Concept

Assume concepts are independent

2) Clustering

K-means

Fuzzy C-means

Hierarchical clustering

Mixture of Gaussians

http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/index.html

3) Group Meaningful Context13

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),(distance)|( ccgccgP

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3) Music Abstraction

My Fist Your Face

Rose

I've Got to See You Again

Use annotations

rock

alternative

seen live

indie

90s

electro

romance

Thinking of You

Sleeping Beauty

jazz

Sleeping Beauty {(rock, w1), (90s, w2)}Rose {(rock, w1), (indie, w2)}

Weight calculation

|}in |{|

||log

) in (

)in (, sdcs

DC

sdccount

sdccountw

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i

ijdcs ji

song

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5.0|}in |{|log

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sdcs

sdcsN

avgdl

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ktfw

j

j

dcs

dcs

dcs

ji

ji

ji

dci

ijdcs sdccount

sdccounttf

ji ) in (

)in (,

annotations

…

Tf-idf

BM25

ModelingS = {(t, w) | t ∈ T, w ∈ R and 0 ≤ w ≤1}


4) Music Grouping

Similar to context grouping

1)

2)

3)

15

mgwcmmgP .)|(

),(distance)|( mmgmmgP

?)|( mmgP


context music

5) User Profiling

0.5 0.3 0.9 0.8

User’s listen logAbstracted context

0.9 0.3 0.20.8 0.50.7 0.6 0.7

0.6 0.70.4 0.3 0.40.1 0.8 0.7 0.5

Item groups

con

text

gro

up

s

User profile

mg1

cg2

cg1

cgm

mg2

mg2

c m

… …

…


5) User Profiling – Fuzzy Join and Aggrega-tion

17

User Time Music

urisj27 2008.02.25 8:30 Beautiful Day

Music Tag Fuzziness

Beautiful Day Dreamy

User listen logs with context

Music with annotations

Concept Fuzzy Function

Morning

Afternoon

… ...

Context conceptsand fuzzy function

User Time Music Context Fuzziness

urisj27 2008.02.25

8:30Beautiful Day Morning 1.0

Fuzzy-equivalent Join (Time)

Equivalent Join (Music)

User Context DomainFuzzi-ness

Fuzziness

urisj27 Morning Dreamy 1.0

User Context Domainp(mg|cg,u)

urisj27 Morning Dreamy 0.7

Aggregation

i

ji

ji

cgcgluulLl

mgmglcgcgluulLl

ijmgcgu fuzzinessl

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ucgmgpw

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,, .

.

),|(

},,|),,,({ MGmgCGcgUufuzzinessmgcgulL

Context groupingItem grouping


Contribution

Model based context aware recommendation

Do not depends on ambiguous relationships among concepts, users, and items

Not from the name or description

But from the semantic annotations, tags

Abstract context concepts by using fuzzy membership func-tions

Distinguish context concepts from domain concepts

There is no reason to put them together

Even though they have the same name, we have to consider them as different.

– Domain concepts are only meaningful when they are used in that domain. They may have different meaning when they are used in different domains.


How to Evaluate?

How to evaluate effect of the context?

Divide logs into training set and test set

Give the same information and see the results of no context using path and context using path

– If recommended song list contains the song, it’s ok.

– Top k recommendation results.


Experiments

Two domains

Music domain

– Last.fm

Movie domain

– iMDB

They has different characteristics

20


Publication Schedule

Target conference

The 2009 IEEE/WIC/ACM International Conference on Web Intelligence (WI ’09)

– Info: 15-18 September 2009, Milan Italy

– Due date: April 10, 2009

– Notification: June 3, 2009

– Format: IEEE 2 column format, max 8 pages


Deep Research Topic

1) What is the important features of context and music?

2) What is the optimal model?

22

context music


Additional Issues

Crawling

Data sampling

Relationship extraction

Approximate string matching


Crawling last.fm

735,000 users

South Korea, North Korea, Japan, United Kingdom, USA

5,855,000 tracks

duplicated multiple tracks

913,720/3,322,000 …… still crawling

69,725,000 user listen recent tracks

69,000,000 listen tracks of thousands of users

6,659,000 user loved tracks

2,311,000 user tags


Data sampling

미국 국적에 음악을 많이 들은 상위 100 여명 정도에 대해서만 테스트 select * from lfm_user where country = 'United States' and

track_count2 > 1000 order by track_count2 desc

상위 100 여명 정도가 많이 들은 노래 선정 select * from lfm_rel_user_track_2 where user_id = 'thetas-

teofink‘

상위 100 여명 정도가 많이 들은 노래에 있는 tag 로 음악 추상화 Artist, album 을 어떻게 활용할지는 일단 보류

앨범 이름 , 곡명이 일치하지 않는 것 어떻게 처리할지 고려하자 .

Approximate string matching 을 적용하는 것은 또 다른 문제

25


Approximate String Matching

Not exact link data – Approximate string matching

Data were represented by user’s song name so that same song have multiple names.

Last.fm does not assume strict foreign key constraints.

26

u2

u1

m3

m1

u4

m2

u3

m4

User

U1

U2

U3

U4

Music

M1

M2

M3

M4

User Music Time

U1 M1

U1 M3

U2 M2’

U2 M4’

We have to match by us-ing fuzzy match methods


Approximate String Matching

Approximate string search

Levenshtein Distance (Edit Distance)

This calculates the minimum number of insertions, deletions, and substitutions necessary to convert one string into another.

http://www.merriampark.com/ld.htm

Gestalt

SoundEx

Its goal is to group letters that sound alike, then convert the name into a series of numbers that can represent the name

Jaccard Similarity

http://en.wikipedia.org/wiki/Jaccard_Similarity_Coefficient

Cosine Similarity

http://en.wikipedia.org/wiki/Cosine_similarity

Dice Similarity

http://en.wikipedia.org/wiki/Dice%27s_coefficient

27

http://www.merriampark.com/ld.htm

http://en.wikipedia.org/wiki/Jaccard_Similarity_Coefficient

http://en.wikipedia.org/wiki/Cosine_similarity


Packages

FLAMINGO Package

http://flamingo.ics.uci.edu/releases/2.0/

http://flamingo.ics.uci.edu/releases/2.0/


Additional Research Topics

Approximate string matching for Korean

한글은 초성 , 중성 , 종성으로 나누어지기 때문에 글자 단위가 아니라 , 이 같은 음소 단위로 처리 해야할 필요가 있음 .

동일한 발음을 가지는 ‘ㅔ’ ,’ ㅐ’ 등의 모음 처리 및 ‘ㄷ’ ,’ ㅌ’ 등의 자음 받침에 대한 고려가 필요함 .

What can it be used for?

검색어 추천 및 맞춤법 교정

한글 데이터가 포함된 Data mining

웹 상의 자료는 오타 , 맞춤법 오류 등이 많으므로 이를 고려해야 함

Documents

Context-Aware Recommendation