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

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ORDER DATA

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ORDER DATA (cont.)

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ORDER DATA (cont.)

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VECTOR & DIMENSION

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VECTOR & DIMENSION

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VECTORS

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VECTORS

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SIMILARITY CALCULATION

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USER SIMILARITY MATRIX

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SIMILARITY CALCULATION

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SIMILARITY CALCULATION

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SIMILARITY CALCULATION EXAMPLE

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K-NEAREST-NEIGHBOR

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K-NEAREST-NEIGHBOR

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NEIGHBORS’ ORDER

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REMOVE BOUGHT ITEMS

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CALCULATING FINAL SCORE

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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

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OBJECT INFORMATION

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FEATURE SET

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SIMILARITY MATRIX

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SIMILARITY MEASURE

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SIMILARITY MEASURE

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SIMILARITY MATRIX

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SIMILARITY SORTING

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K-NEAREST NEIGHBOR (knn)

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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