Model-based Context-Aware Recommendation

Model-based 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


Recommendation

Recommendation

Context-Aware Recommendation

Model-based Context-Aware Recommendation

1) Context abstraction

2) Item abstraction

3) Model construction

1) Context-item association rule mining

2) User profiling

3

)|()( umPmscore like

),|()( ucmPmscore like


Model-based Context-Aware Recommenda-tion

4

mg1

User

Item

cg2

cg1

cgm

User and ContextCluster

mg2

mgn

… … …

ItemCluster

MGmg CGcg

MGmg

like

uccgPcgmgPmgmP

ucmgPmgmP

ucmPmscore

),|()|()|(

),|()|(

),|()(

Active context

1

32

132

Model based recommendation

groupcontext :

group item :

context :

user :

item target :

cg

mg

c

u

m


r1: (Male, 40대 , Winter, Home) -> (Jazz, 16Bit); <0.07, 0.7>r2: (20대 , Night) -> (Rock); <0.03, 0.3>r3: (20대 , Night) -> (Dance); <0.02, 0.4>

Learning from Logs

5

Users listen logs

User Context MusicAge Gen-

derJob … Time Loca-

tion… Title Genr

eBit Artis

tAl-

bum…

… … M1

Materialize

User Context MusicMale

Fe-male

10대

… Win-ter

Home

… Rock Jazz Bal-lad

16bit

4bit …

1.0 0.0 0.9 0.8 1.0 1.0 0.2 0.0 0.0 1.0

…

FuzzyAbstraction

(Quantization)

Learning (association rule mining)

Title Genre

Bit Artist

Al-bum

…

Users MusicUser Music Time Location

u1 … M1

u1 …

u2

u1

u3

u3

u2

Age Gen-der

Job …

… …

…

mg1cg1

cg2

cgm

mg2

mgn

Scoring

uc, m


Definition

Database Definition

Fuzzy Set

6

j

j

aj

mji

aj

mj

ni

Dv

vvvvt

Da

aaaaA

ttttT

),...,,...,,(

: attribute ofDomain

},...,,...,,{

},...,,...,,{

21

21

21

m

ja

lakaaaa

jf

jkaka

j

jjjjj

kja

jj

FF

ffffF

mvm

matfmTtmtf

1

,,2,1,

,,

},...,,...,,{

]1,0[:

}]),[(,|)),{(

,


Definition (cont’d)

Fuzzy Predicate

Fuzzy Association Rule

r indicate that if pA (t) satisfied, we can imply pB (t). This means that if a

tuple t is a member of fuzzy set A, we can say it is also a member of fuzzy set B.

Association Rule Mining

The goal of association rule mining is to find all association rules that has confidence and support bigger than or equal to minimum confidence and minimum support.

7

kaF jkjaFttp ,:)(

,

})(,)(|{

)()(:

rconfidencersupportrR

tptpr BA


Use Fuzzy Association Rule in Recommenda-tion

8


Learning: Association Rule Mining

9

User Context MusicMale

Fe-male

10대

20대

Win-ter

… Fall Morn-ing

Night

Home

Of-fice

Warm Cold … Rock Jazz Bal-lad

16bit

4bit …

1.0 0.0 0.9 0.1 0.8 1.0 1.0 0.0 0.8 0.3 1.0 0.2 0.0 0.0 1.0

1.0 0.0

Example


Formal Expression

10

Copyright 2008 by CEBT 11

1) Context Abstraction – Context Representation

Filter

sensed data

filtered data

concept

context

context

context

context

Coolcontext

Filter

Sensor

Sensor

Context Concepts

fuzzy membership functionfiltered data

20080910 14:34:00, 7℃= {(cool, 0.5), (cold, 0.5),(fall,0.9),(afternoon,1.0),…}

…

}10 ,|),{( wandRwCCccwccc

Calculate weight by using fuzzy membership functions


1) Context Abstraction – Fuzzy Join

12

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

13

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

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


Context Concept and Membership Function

type idtemporal con-

ceptsbase index a b

c ( 주기 )

d alpha-minalpha-max

f(x) = max(min(a * cos( 2pi * (x - b ) / c) - d, 1, 0)

season

1 spring 2008-01-01 00:00 0 3 151200 525600 1.7 88663 213737 f(x) = max(min( 3 * cos( 2pi * ( x - 151200 ) / 525600) - 1.7, 1,) 0)2 summer 2008-01-01 00:00 0 3 280800 525600 1.7 218263 343337 f(x) = max(min( 3 * cos( 2pi * ( x - 280800 ) / 525600) - 1.7, 1,) 0)3 autumn 2008-01-01 00:00 0 3 410400 525600 1.7 347863 472937 f(x) = max(min( 3 * cos( 2pi * ( x - 410400 ) / 525600) - 1.7, 1,) 0)4 winter 2008-01-01 00:00 0 3 21600 525600 1.7 -40937 84137 f(x) = max(min( 3 * cos( 2pi * ( x - 21600 ) / 525600) - 1.7, 1,) 0)

month

5 feburary 2008-01-01 00:00 0 20 21600 525600 18.5 -4965 48165 f(x) = max(min( 20 * cos( 2pi * ( x - 21600 ) / 525600) - 18.5, 1,) 0)

6 january 2008-01-01 00:00 0 20 66240 525600 18.5 39675 92805 f(x) = max(min( 20 * cos( 2pi * ( x - 66240 ) / 525600) - 18.5, 1,) 0)

7 march 2008-01-01 00:00 0 20 106560 525600 18.5 79995 133125 f(x) = max(min( 20 * cos( 2pi * ( x - 106560 ) / 525600) - 18.5, 1,) 0)

8 april 2008-01-01 00:00 0 20 149760 525600 18.5 123195 176325 f(x) = max(min( 20 * cos( 2pi * ( x - 149760 ) / 525600) - 18.5, 1,) 0)

9 may 2008-01-01 00:00 0 20 194400 525600 18.5 167835 220965 f(x) = max(min( 20 * cos( 2pi * ( x - 194400 ) / 525600) - 18.5, 1,) 0)

10 june 2008-01-01 00:00 0 20 239040 525600 18.5 212475 265605 f(x) = max(min( 20 * cos( 2pi * ( x - 239040 ) / 525600) - 18.5, 1,) 0)

11 july 2008-01-01 00:00 0 20 282240 525600 18.5 255675 308805 f(x) = max(min( 20 * cos( 2pi * ( x - 282240 ) / 525600) - 18.5, 1,) 0)

12 august 2008-01-01 00:00 0 20 326880 525600 18.5 300315 353445 f(x) = max(min( 20 * cos( 2pi * ( x - 326880 ) / 525600) - 18.5, 1,) 0)

13 september 2008-01-01 00:00 0 20 371520 525600 18.5 344955 398085 f(x) = max(min( 20 * cos( 2pi * ( x - 371520 ) / 525600) - 18.5, 1,) 0)

14 october 2008-01-01 00:00 0 20 416160 525600 18.5 389595 442725 f(x) = max(min( 20 * cos( 2pi * ( x - 416160 ) / 525600) - 18.5, 1,) 0)

15 november 2008-01-01 00:00 0 20 459360 525600 18.5 432795 485925 f(x) = max(min( 20 * cos( 2pi * ( x - 459360 ) / 525600) - 18.5, 1,) 0)

16 december 2008-01-01 00:00 0 20 504000 525600 18.5 477435 530565 f(x) = max(min( 20 * cos( 2pi * ( x - 504000 ) / 525600) - 18.5, 1,) 0)

specialday

17 new year's day 2008-01-01 00:00 0 365 720 525600 364.2 -2672 4112 f(x) = max(min( 365 * cos( 2pi * ( x - 720 ) / 525600) - 364.2, 1,) 0)

18 valentine's day 2008-01-01 00:00 0 365 64080 525600 364.2 60688 67472 f(x) = max(min( 365 * cos( 2pi * ( x - 64080 ) / 525600) - 364.2, 1,) 0)

19 christmas 2008-01-01 00:00 0 365 516420 525600 363.8 511443 521037 f(x) = max(min( 365 * cos( 2pi * ( x - 516420 ) / 525600) - 363.8, 1,) 0)

day of week

20 Monday 2008-01-01 00:00 1440 7 720 10080 5.6 -102 1542 f(x) = max(min( 7 * cos( 2pi * ( x - 720 ) / 10080) - 5.6, 1,) 0)21 Tuesday 2008-01-01 00:00 1440 7 2160 10080 5.6 1338 2982 f(x) = max(min( 7 * cos( 2pi * ( x - 2160 ) / 10080) - 5.6, 1,) 0)22 Wednesday 2008-01-01 00:00 1440 7 3600 10080 5.6 2778 4422 f(x) = max(min( 7 * cos( 2pi * ( x - 3600 ) / 10080) - 5.6, 1,) 0)23 Thursday 2008-01-01 00:00 1440 7 5040 10080 5.6 4218 5862 f(x) = max(min( 7 * cos( 2pi * ( x - 5040 ) / 10080) - 5.6, 1,) 0)24 Friday 2008-01-01 00:00 1440 7 6480 10080 5.6 5658 7302 f(x) = max(min( 7 * cos( 2pi * ( x - 6480 ) / 10080) - 5.6, 1,) 0)25 Saturday 2008-01-01 00:00 1440 7 7920 10080 5.6 7098 8742 f(x) = max(min( 7 * cos( 2pi * ( x - 7920 ) / 10080) - 5.6, 1,) 0)26 Sunday 2008-01-01 00:00 1440 7 9360 10080 5.6 8538 10182 f(x) = max(min( 7 * cos( 2pi * ( x - 9360 ) / 10080) - 5.6, 1,) 0)27 holiday 2008-01-01 00:00 1440 7 8496 10080 4 7059 9933 f(x) = max(min( 7 * cos( 2pi * ( x - 8496 ) / 10080) - 4, 1,) 0)

time of day

28 dawn 2008-01-01 00:00 0 24 240 1440 22.5 174 306 f(x) = max(min( 24 * cos( 2pi * ( x - 240 ) / 1440) - 22.5, 1,) 0)29 sunrise 2008-01-01 00:00 0 24 360 1440 22.5 294 426 f(x) = max(min( 24 * cos( 2pi * ( x - 360 ) / 1440) - 22.5, 1,) 0)30 morning 2008-01-01 00:00 0 12 480 1440 10.5 386 574 f(x) = max(min( 12 * cos( 2pi * ( x - 480 ) / 1440) - 10.5, 1,) 0)31 forenoon 2008-01-01 00:00 0 6 720 1440 22.5 466 734 f(x) = max(min( 6 * cos( 2pi * ( x - 720 ) / 1440) - 22.5, 1,) 0)32 noon 2008-01-01 00:00 0 24 720 1440 22.5 654 786 f(x) = max(min( 24 * cos( 2pi * ( x - 720 ) / 1440) - 22.5, 1,) 0)33 afternoon 2008-01-01 00:00 0 6 900 1440 4.5 766 1034 f(x) = max(min( 6 * cos( 2pi * ( x - 900 ) / 1440) - 4.5, 1,) 0)34 sunset 2008-01-01 00:00 0 24 180 1440 22.5 1014 1146 f(x) = max(min( 24 * cos( 2pi * ( x - 180 ) / 1440) - 22.5, 1,) 0)35 evening 2008-01-01 00:00 0 8.5 1140 1440 5.5 960 1320 f(x) = max(min( 8.5 * cos( 2pi * ( x - 1140 ) / 1440) - 5.5, 1,) 0)36 night 2008-01-01 00:00 0 4 0 1440 2.3 -182 182 f(x) = max(min( 4 * cos( 2pi * ( x - 0 ) / 1440) - 2.3, 1,) 0)37 midnight 2008-01-01 00:00 0 6 60 1440 4.7 -60 180 f(x) = max(min( 6 * cos( 2pi * ( x - 60 ) / 1440) - 4.7, 1,) 0)

15

f(x) = max(min(a * cos( 2pi * (x - b ) / c) - d, 1, 0)


Fuzzy join query

16

select log_id, concept_id, fuzziness from ( select t2.id log_id, t2.user_id, t2.track_id, t1.id concept_id, t1.name, t2.m_date, t2.m_val, case when t1.a*cos(2* 3.141592 *(t2.M_VAL + t1.IDX-t1.b)/t1.c) - t1.d > 1 then 1 when t1.a*cos(2* 3.141592 *(t2.M_VAL + t1.IDX-t1.b)/t1.c) - t1.d < 0 then 0 else t1.a*cos(2* 3.141592 *(t2.M_VAL + t1.IDX-t1.b)/t1.c) - t1.d end fuzziness from test_lfm_temp_concept t1, test_lfm_user_track t2 order by log_id asc ) where fuzziness > 0.5


3) Music Abstraction – Music Representation

My Fist Your Face

Rose

I've Got to See You Again

rock

alternative

seen live

indie

90s

electro

romance

Thinking of You

Sleeping Beauty

jazz

Sleeping Beauty = {(rock, 1.0), (90s, 0.9)}Rose = {(rock, 1.0), (indie, 0.8)}

…

Membership degree calculation

song annotations

Representation

}10 ,|),{( wandRwDCdcwdcm

DC: Domain concepts

m: Target items

0005.0log3

0005.0log3)) min ((max

)min (log

2

22

,

idc

ij

dcm

dccount

dccount

wji


Fuzzy join query

18

select a.track_id, c.id tag_id, a.tag, a.count/b.tag_count_max maxratio, (log(2, a.count/b.tag_count_max) + 5 - log(2, 0.0005))/(5-log(2,0.0005)) fuziness from test_lfm_track_tag a join test_lfm_track b on a.track_id = b.id join test_lfm_tag c on a.tag = c.tag where a.count/b.tag_count_max > 0.0001


5) User Profiling – Fuzzy Join and Aggrega-tion

19

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

fuzzinessl

ucgmgpw

..

...

,, .

.

),|(

},,|),,,({ 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 target 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.


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


Additional Issues

Crawling

Data sampling

Relationship extraction

Approximate string matching – didn’t apply


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 = 'thetasteofink‘

Tag 읽어오는 쿼리 select a.id, a.artist, a.name, b.tag, b.count from

test_lfm_track_match a join lfm_rel_track_tag b on a.id = b.track_id

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


Experiments

Two domains

Music domain

– Last.fm

Movie domain

– iMDB

They have different characteristics

26


Data Sets

Data Table Name Description Size

User TEST_MST_USER 사용자 정보 92

Track TEST_MST_TRACK 곡 정보 ( 사용자가 듣지 않은 곡은 배제 ) 74058

Tag TEST_MST_TAG 태그 245092

Track-tag TEST_REL_TRACK_TAG_FUZZINESS 곡에 부착된 태그 2125879

User listen logs TEST_REL_USER_TRACK 사용자가 음악을 들은 로그 587037

Temporal concepts TEST_MST_TEMP_CONCEPT시간에 관련된 개념 및 시간 값으로부터 각 개념에 대한 소속 값을 구하기 위한 membership degree func-tion

37

Log-temporal con-cepts

TEST_REL_LOG_CONCEPT_FUZZINESS

각 로그 시점에서 관련된 시간 개념과 이에 소속 값 2929362

Sampling from last.fm

Documents

Model-based Context-Aware Recommendation