tangent tangent a novel, “surprise-me”, recommendation algorithm kensuke onuma : sony...

TANGENTTANGENTA Novel, “Surprise-me”, Recommendation Algorithm

Kensuke Onuma : Sony CorporationHanghang Tong : Carnegie Mellon Univ.

Christos Faloutsos : Carnegie Mellon Univ.

2

Motivation

Go off on a ‘TANGENT’ !

Movies

KevinKevinLizLiz

TimTim

MarkMark

JessicaJessica

MaryMary

JohnJohn

RachelRachel

BobBobMikeMike

TomTom

Broadening users’ horizon

More chance to increase sales of items

3

What we want are …

user

movie

comedy fans horror fans

Conventional recommendationalgorithms’ answer TANGENT’s answer

A

target user(= query node)

4

Outline• Motivation• Problem definition• Algorithm• Experiments• Conclusion

5

Graphs for recommendation[bipartite graph]

John Mike

A B C D

Mark Rachel Tom Mary

E F G H

1V

2V

E

),( EVG : weighted based on rating

V E: users and movies 21,VVV

6

Problem definition of TANGENTGiven: - An edge-weighted undirected graph with adjacency matrix - The set of query nodes

Given: - An edge-weighted undirected graph with adjacency matrix - The set of query nodes

Find: - A node that satisfy following conditions. (1) Close enough to (2) Possessing high potential to reach other nodes

Find: - A node that satisfy following conditions. (1) Close enough to (2) Possessing high potential to reach other nodes

AG

kiiqQ 1)(

Q

QG

user

movie

7


8

Outline of TANGENT algorithm

1. Calculate relevance score of each node to 2. Calculate bridging score of each node3. Compute the TANGENT score

by merging two criteria above

Qr

Q

b

Qt

Quser

movie

9

[Step 1] Relevance scoreRandom walk with restart [Pan+ KDD ’04]

1

2

3

4 5

6

7

8

9

querynode

node

1 0.577

2 0.132

3 0.123

4 0.123

5 0.036

6 0.001

7 0.006

8 0.001

9 0.001

ir ,1

R nrrr

21

Various Scalable Solution [Tong ’06] - OnTheFly - B_Lin - NB_Lin - BB_Lin (for bipartitle graph)

Various Scalable Solution [Tong ’06] - OnTheFly - B_Lin - NB_Lin - BB_Lin (for bipartitle graph)

10

[Step 2] Bridging score (Intuition)

1

2 34

5

7 6

12

34

5

7

6

7,77,67,57,47,37,2

6,76,66,56,46,36,2

5,75,65,55,45,35,2

4,74,64,54,44,34,2

3,73,63,53,43,33,2

2,72,62,52,42,32,2

1

rrrrrr

rrrrrr

rrrrrr

rrrrrr

rrrrrr

rrrrrr

R

7,77,67,57,47,37,2

6,76,66,56,46,36,2

5,75,65,55,45,35,2

4,74,64,54,44,34,2

3,73,63,53,43,33,2

2,72,62,52,42,32,2

1

rrrrrr

rrrrrr

rrrrrr

rrrrrr

rrrrrr

rrrrrr

R

a node in a group a node between groups

~0

~0

1b 1bsmall large

11

[Step 2] Bridging score (Detail)

),,,,,mean(

1

6,44,66,22,64,22,41 rrrrrrb

1

2 3

4

neighbors

6,66,46,2

4,64,44,2

2,62,42,2

rrr

rrr

rrr

1R

1Sr

12

[Step 3] TANGENT score

Si

jQjjQjQ r

rbrt

ˆ,

,,

A. Simple multiplication. (not linear combination, not skyline query, )

user

movie

query

relevance scoreto query nodes

relevance scoreamong neighbors

13

Example

1

2

3

4 5

6

7

8

9

node

1 0.577 8.579 4.949

2 0.132 8.579 1.129

3 0.123 11.085 1.362

4 0.123 11.085 1.362

5 0.036 20.789 0.755

6 0.001 7.967 0.010

7 0.006 12.847 0.074

8 0.001 7.967 0.010

9 0.001 7.967 0.010

querynode

ibir ,1 it ,1

Group 1 Group 2

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Outline• Motivation• Problem definition• Algorithm• Experiments

– Synthetic data– Real data

• MovieLens (user-movie)• DBLP (author-paper)

• Conclusionon our paper

15

Synthetic data[bipartite graph]

1 2 3 4 5 6 7 8 9

10 11 1213 14 15 16 17 18 19 20 21 22 23

24 25 26

query No.1 in TANGENT

node 1 node 16

node 5 node 20

node 12 node 20

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Real data [MovieLens]User Preference (rating 5)- A Nightmare on Elm Street (1984) (Horror)- The Shining (1980) (Horror)- Jaws (1975) (Action, Horror)

Rank Title Genre

1 The Silence of the Lamb (1991) Dr, Thr

2 Psycho (1960) Hor, Rom, Thr

3 Pulp Fiction (1994) Cr, Dr

4 An American Werewolf in London (1981)

Hor

5 Natural Born Killers (1994) Ac, Thr

6 Carrie (1976) Hor

7 Alien (1979) Ac, Hor, SF, Thr

8 Twelve Monkeys (1995) Dr, SF

9 Evil Dead II (1987) Ac, Ad, Com, Hor

10 Scream (1996) Hor, Thr

15 Star Wars (1977) Ac,Adv,Rom,SF,War

17 Fargo (1996) Cr, Dr, Thr

22 The Godfather (1972) Ac, Cr, Dr

45 Contact (1997) Dr, SF

Rank Title Genre

1 The Silence of the Lambs (1991) Dr, Thr

2 Scream (1996) Hor, Thr

3 Pulp Fiction (1994) Cr, Dr

4 Star Wars (1977) Ac, Adv, Rom, SF, War

5 Fargo (1996) Cr, Dr, Thr

6 Twelve Monkeys (1995) Dr, SF

7 Psycho (1960) Hor, Rom, Thr

8 The Godfather (1972) Ac, Cr, Dr

9 Contact (1997) Dr, SF

10 Alien (1979) Ac, Hor, SF, Thr

13 An American Werewolf in London (1981)

Hor

12 Natural Born Killers (1994) Ac, Thr

16 Carrie (1976) Hor

23 Evil Dead II (1987) Ac, Ad, Com, Hor

Ranked list by relevance score Ranked list by TANGENT score

943 users1682 movies55375 ratings

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Rank Title Genre

1 The Flintstones (1994) Ch,Com

2 Spy Hard (1996) Com

3 Oliver & Company (1988) Ani,Chi

4 Jack (1996) Com,Dr

5 Son in Law (1993) Com

6 Ace Ventura: When Nature Calls (1995)

Com

7 Renaissance Man (1994) Com,Dr,War

8 Pocahontas (1995) Ani,Chi,Mus,Rom

9 Corrina, Corrina (1994) Com,Dr,Rom

10 Beverly Hillbillies, The (1993) Com

11 Princess Bride, The (1987) Ac,Adv,Com,Rom

15 Monty Python and the Holy Grail (1974)

Com

21 Empire Strikes Back, The (1980) Ac,Adv,Dr

26 Raiders of the Lost Ark (1981) Ac,Adv

29 Return of the Jedi (1983) Ac,Adv,Rom,SF,War


42 Toy Story (1995) Ani,Chi,Com

53 Men in Black (1997) Com,Dr

Rank Title Genre


2 Return of the Jedi (1983) Ac,Adv,Rom,SF,War

3 The Princess Bride (1987) Ac,Adv,Com,Rom

4 Toy Story (1995) Ani,Chi,Com

5 Monty Python and the Holy Grail (1974)

Com

6 Spy Hard (1996) Com

7 Raiders of the Lost Ark (1981) Ac,Adv

8 Empire Strikes Back, The (1980) Ac,Adv,Dr

9 Jack (1996) Com,Dr

10 Men in Black (1997) Ac,Adv,Com,SF

25 Ace Ventura: When Nature Calls (1995)

Com

27 Corrina, Corrina (1994) Com,Dr,Rom

35 Son in Law (1993) Com

42 Oliver & Company (1988) Ani,Chi

43 Renaissance Man (1994) Com,Dr,War

52 Pocahontas (1995) Ani,Chi,Mus,Rom

166 The Beverly Hillbillies (1993) Com

1439 The Flintstones (1994) Ch,Com

relevance score TANGENT score

User Preference (rating 5)- Robin Hood: Men in Tights (1993) (Comedy)- Young Frankenstein (1974) (Comedy, Horror)- Naked Gun 33 1/3: The Final Insult (1994) (Comedy)- Fatal Instinct (1993) (Comedy)

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Conclusion• Definition of a novel recommendation problem

– “how to make a recommendation that broadens the horizons of the user?”

– [Approach]* close to the user preferences * have high connectivity to other groups

• Design of algorithm– “Relevance score” X “Bridging score”– Effective & Efficient

• Experiments– synthetic dataset– real dataset

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Thank youKensuke [email protected]

Hanghang [email protected]

Christos [email protected]

Poster tonight !19:30 – 22:00

at Hôtel de Ville

Code availablehttp://www.cs.cmu.edu/~kensuke/

mailto:[email protected]



tangent tangent a novel, “surprise-me”, recommendation algorithm kensuke onuma : sony...

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