recommender system

Behind the sceene aRECOMMENDER SYSTEM

TechTalk #51

Arif Akbarul Huda

increasing information data

filtering content

user perspektive

are you familiar.. ?

why do we need a recommender engine?

• Increase the number of items sold• Sell more diverse items• Increase the user satisfaction• Increase user fidelity• Better understand what the user

wants

a recommendation system...how its work?

Recommender system (RS) help users find items (e.g., news items, movies)

that meet their specific needs.

3 common approach

1.collaborative filtering

2.content-based filtering

3.hybrid recommender system

Content Based Filtering

collaborative filtering

a method of making automatic predictions (filtering) about the interests of a user by collecting

preferences or taste information from many users (collaborating)

USER & ITEM

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

13

ORDER DATA

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

14

ORDER DATA (cont.)

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

15

ORDER DATA (cont.)

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

16

VECTOR & DIMENSION

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

17

VECTOR & DIMENSION

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

18

VECTORS

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

19

VECTORS

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

20

SIMILARITY CALCULATION

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

21

USER SIMILARITY MATRIX

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

22

SIMILARITY CALCULATION

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

23

SIMILARITY CALCULATION

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

24

SIMILARITY CALCULATION EXAMPLE

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

25

K-NEAREST-NEIGHBOR

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

26

K-NEAREST-NEIGHBOR

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

27

NEIGHBORS’ ORDER

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

28

REMOVE BOUGHT ITEMS

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

29

CALCULATING FINAL SCORE

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

Content Based Filtering

Content Based Filtering

based on a description of the item and a profile of

the user’s preference (Brusilovsky Peter , 2007)

OBJECT

http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system

33

OBJECT INFORMATION

http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1

34

FEATURE SET

http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1

35http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1

36

SIMILARITY MATRIX

http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1

37

SIMILARITY MEASURE

http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1

38

SIMILARITY MEASURE

http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1

39

SIMILARITY MATRIX

http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1

40

SIMILARITY SORTING

http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1

41

K-NEAREST NEIGHBOR (knn)

http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1

Hybrid

Hybrid

• CF+CB• CF+ context-aware• CF+CB+Demographic• .....

my research....

a foodfood has characteristic

of taste (measure by level) :

- sweet

- bitter

- savory

- salty

- sour

- spicy

- sauce

- meat

- vegetable

user

item

• previous taste preference• current location • Restoran => foods

recommended item

- Restoran with foods that meet user taste preferences

feedback

• rating• comment• comment

a model...

end