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ARTIFICIAL INTELLIGENCE CASE-BASED REASONING FOR RECOMMENDER SYSTEMS PROF THOMAS ROTH-BERGHOFER, UNIVERSITY OF WEST LONDON, UK
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Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

Aug 12, 2015

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Page 1: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

A R T I F I C I A L I N T E L L I G E N C E C A S E - B A S E D R E A S O N I N G F O R R E C O M M E N D E R S Y S T E M S

P R O F T H O M A S R O T H - B E R G H O F E R , U N I V E R S I T Y O F W E S T L O N D O N , U K

Page 2: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

– S H A N TA N U N A R AY E N , C E O , A D O B E

“We as consumers expect relevant, interactive, personalised material wherever we are.”

Page 3: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

– D AV I D W A D H W A N I , S E N I O R V I C E P R E S I D E N T, A D O B E

“The new frontier is experience.”

Page 4: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

E X P E R I E N C E

Page 5: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

S P E C I F I C K N O W L E D G E O B TA I N E D F R O M A S P E C I F I C P R O B L E M S O LV I N G C O N T E X T

E X P E R I E N C E

Page 6: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

S P E C I F I C K N O W L E D G E O B TA I N E D F R O M A S P E C I F I C P R O B L E M S O LV I N G C O N T E X T

E X P E R I E N C E

© U.S. Army Corps of Engineers - CC BY 2.0 https://www.flickr.com/photos/usacehq/10019330364

Page 7: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

S P E C I F I C K N O W L E D G E O B TA I N E D F R O M A S P E C I F I C P R O B L E M S O LV I N G C O N T E X T

E X P E R I E N C E

© U.S. Army Corps of Engineers - CC BY 2.0 https://www.flickr.com/photos/usacehq/10019330364

© Craig Breil - CC BY 2.0https://www.flickr.com/photos/umich-msis/6550246787

Page 8: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

S P E C I F I C K N O W L E D G E O B TA I N E D F R O M A S P E C I F I C P R O B L E M S O LV I N G C O N T E X T

E X P E R I E N C E

© U.S. Army Corps of Engineers - CC BY 2.0 https://www.flickr.com/photos/usacehq/10019330364

© Craig Breil - CC BY 2.0https://www.flickr.com/photos/umich-msis/6550246787

https://www.flickr.com/photos/marceldouwedekker/© Christoph Weigel - CC BY-SA 2.0

Page 9: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

E X A M P L E S

Page 10: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

E X A M P L E S

• Printer repairman remembers a similar failure of printer in the past.

Page 11: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

E X A M P L E S

• Printer repairman remembers a similar failure of printer in the past.

Problem: My printout has white streaks. I printed on paper. Cleaning the printer did not help.

Solution: Ink cartridge was low on toner. ➜ Replace ink cartridge.

Page 12: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

E X A M P L E S

• Printer repairman remembers a similar failure of printer in the past.

• A doctor remembers the symptoms of a bacterial infection from another patient.

Problem: My printout has white streaks. I printed on paper. Cleaning the printer did not help.

Solution: Ink cartridge was low on toner. ➜ Replace ink cartridge.

Page 13: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

E X A M P L E S

• Printer repairman remembers a similar failure of printer in the past.

• A doctor remembers the symptoms of a bacterial infection from another patient.

Problem: My printout has white streaks. I printed on paper. Cleaning the printer did not help.

Solution: Ink cartridge was low on toner. ➜ Replace ink cartridge.

Problem: Patient X has fever. Blood test shows high count of white blood cells. Patient X tells about holidays in central Africa.

Solution: Tropical infection possible ➜ Prescribe further blood tests.

Page 14: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

E X A M P L E S

• Printer repairman remembers a similar failure of printer in the past.

• A doctor remembers the symptoms of a bacterial infection from another patient.

Problem: My printout has white streaks. I printed on paper. Cleaning the printer did not help.

Solution: Ink cartridge was low on toner. ➜ Replace ink cartridge.

Problem: Patient X has fever. Blood test shows high count of white blood cells. Patient X tells about holidays in central Africa.

Solution: Tropical infection possible ➜ Prescribe further blood tests.

[ ]Problem +

SolutionCase =

Page 15: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

E X A M P L E S

• Printer repairman remembers a similar failure of printer in the past.

• A doctor remembers the symptoms of a bacterial infection from another patient.

Problem: My printout has white streaks. I printed on paper. Cleaning the printer did not help.

Solution: Ink cartridge was low on toner. ➜ Replace ink cartridge.

Problem: Patient X has fever. Blood test shows high count of white blood cells. Patient X tells about holidays in central Africa.

Solution: Tropical infection possible ➜ Prescribe further blood tests.[ ]Problem

+ Solution

Case =

Page 16: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

– D AV I D B . L E A K E , I N D I A N A U N I V E R S I T Y, U S A

“CBR is reasoning by remembering.”

Case-based reasoning is a cognitive approach for modelling human problem solving behaviour.

Page 17: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

– A G N A R A A M O D T, N T N U , N O R W AY, A N D E N R I C P L A Z A , I I I A , S PA I N

“CBR is an approach to problem solving and learning.”

Case-based reasoning is an engineering approach for developing and implementing

intelligent systems.

Page 18: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

Old Problem

B A S I C I D E A• Retrieve relevant experience from the case base.

• Re-use retrieved experience in the context of the current problem.

• Store new experience in the case base – learn.

New Problem Old Problem

Old Solution Case

i

Case Base

Page 19: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

Old Problem

B A S I C I D E A• Retrieve relevant experience from the case base.

• Re-use retrieved experience in the context of the current problem.

• Store new experience in the case base – learn.

New Problem Old Problem

Old Solution Case

i

Case Base

Similarity

Page 20: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

Old Problem

B A S I C I D E A• Retrieve relevant experience from the case base.

• Re-use retrieved experience in the context of the current problem.

• Store new experience in the case base – learn.

New Problem Old Problem

Old Solution Case

i

Case Base

New Solution

Similarity

Reuse

Page 21: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

New Case

Old Problem

B A S I C I D E A• Retrieve relevant experience from the case base.

• Re-use retrieved experience in the context of the current problem.

• Store new experience in the case base – learn.

New Problem Old Problem

Old Solution Case

i

Case Base

New Solution

Similarity

Reuse

Page 22: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

New Case

Old Problem

B A S I C I D E A• Retrieve relevant experience from the case base.

• Re-use retrieved experience in the context of the current problem.

• Store new experience in the case base – learn.

New Problem Old Problem

Old Solution Case

i

Case Base

New Solution

Similarity

Learn

Reuse

Page 23: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

New Case

Old Problem

B A S I C I D E A• Retrieve relevant experience from the case base.

• Re-use retrieved experience in the context of the current problem.

• Store new experience in the case base – learn.

New Problem Old Problem

Old Solution Case

i

Case Base

New Solution

Similarity

Learn

Reuse

Key aspect: similarity

Page 24: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

New Case

Old Problem

B A S I C R E C O M M E N D E R S Y S T E M

New Problem Old Problem

Old Solution Case

i

Case Base

New Solution

Similarity-based

retrieval

Page 25: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

Products/ServicesRequirements match

New Case

Old Problem

B A S I C R E C O M M E N D E R S Y S T E M

New Problem Old Problem

Old Solution Case

i

Case Base

New Solution

Similarity-based

retrieval

Page 26: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

myCBRCBRT O O L F O R R A P I D P R O T O T Y P I N G O F C B R A P P L I C AT I O N S

Page 27: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

myCBRCBRT O O L F O R R A P I D P R O T O T Y P I N G O F C B R A P P L I C AT I O N S

F O R E X A M P L E : R E C O M M E N D E R S Y S T E M S

Page 28: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

R E C O M M E N D E R S Y S T E M E X A M P L E

Page 29: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

R E C O M M E N D E R S Y S T E M E X A M P L E

Body

Model

Color

Manufacturer

Mileage

Page 30: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015
Page 31: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015
Page 32: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015
Page 33: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015
Page 34: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

S I M I L A R I T Y K N O W L E D G E

Similarity tables

Similarity functions

Page 35: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

S I M I L A R I T Y- B A S E D R E T R I E VA L

Page 36: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

S I M I L A R I T Y- B A S E D R E T R I E VA L

Page 37: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

S I M I L A R I T Y- B A S E D R E T R I E VA L

Page 38: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

S I M I L A R I T Y- B A S E D R E T R I E VA L

Page 39: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

S I M I L A R I T Y- B A S E D R E T R I E VA L

Page 40: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

R E U S E O F A U D I O M I X I N G E X P E R I E N C E

© CC0 1.0 Universal

Page 41: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

R E U S E O F A U D I O M I X I N G E X P E R I E N C E

Case

➜ WorkflowBefore After

© CC0 1.0 Universal

Page 42: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

...

C S V I M P O R T

Colhead1 Colhead2 Colhead3 …

R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …

Page 43: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

...

C S V I M P O R T

Colhead1 Colhead2 Colhead3 …

R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …

Generate attributes1

Page 44: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

...

C S V I M P O R T

Colhead1 Colhead2 Colhead3 …

R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …

Generate attributes1

Page 45: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

...

C S V I M P O R T

Colhead1 Colhead2 Colhead3 …

R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …

Generate attributes1

Set data types2

Page 46: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

...

C S V I M P O R T

Colhead1 Colhead2 Colhead3 …

R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …

Generate attributes1

Set data types2

Page 47: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

...

C S V I M P O R T

Colhead1 Colhead2 Colhead3 …

R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …

Generate attributes1

Boolean

Concept

Float

Integer

String

Symbol

+

Set data types2

Page 48: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

...

C S V I M P O R T

Colhead1 Colhead2 Colhead3 …

R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …

Generate attributes1

...

Set data types2

Page 49: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

...

C S V I M P O R T

Colhead1 Colhead2 Colhead3 …

R1C1 R1C2 R1C3 …

R2C1 R2C2 R2C3 …

… … … …

Generate attributes1

...

Set data types2 Generate instances3

Page 50: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

HTTP : / /MYCBR - PROJECT.NET

Page 51: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

TA K E H O M E M E S S A G E

Page 52: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

TA K E H O M E M E S S A G E

• Case-based reasoning is the method of choice whenever you need to compare products and services.

Page 53: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

TA K E H O M E M E S S A G E

• Case-based reasoning is the method of choice whenever you need to compare products and services.

• myCBR Workbench provides tools to express similarity knowledge and an environment to test your knowledge model.

Page 54: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

TA K E H O M E M E S S A G E

• Case-based reasoning is the method of choice whenever you need to compare products and services.

• myCBR Workbench provides tools to express similarity knowledge and an environment to test your knowledge model.

• myCBR SDK lets you easily integrate similarity-based retrieval in your (mobile) applications.

Page 55: Artificial Intelligence – Case-based reasoning for recommender systems – Invited talk at Techsylvania 2015

A R T I F I C I A L I N T E L L I G E N C E C A S E - B A S E D R E A S O N I N G F O R R E C O M M E N D E R S Y S T E M S

P R O F T H O M A S R O T H - B E R G H O F E R , U N I V E R S I T Y O F W E S T L O N D O N , U K

Prof Dr Thomas Roth-Berghofer Head of Research Cluster Digital Communities University of West London, United Kingdom

[email protected]

T H O M A S . R O T H - B E R G H O F E R @ U W L . A C . U K