recommender system
TRANSCRIPT
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
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
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OBJECT INFORMATION
http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
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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
http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
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SIMILARITY SORTING
http://www.slideshare.net/lonelywolf/recommender-system-content-based-filtering?related=1
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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