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Recommender system

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Arif Huda
Arif Huda

The document discusses recommender systems which help users find items that meet their needs. It describes three common recommendation approaches: collaborative filtering, content-based filtering, and hybrid recommender systems. Collaborative filtering makes predictions about a user's interests based on preferences of many users, while content-based filtering relies on descriptions of items and a user's preference profile. The document also outlines the speaker's research into a hybrid food recommender system that recommends restaurants and foods based on a user's taste preferences and location.

Technology
1 of 46
TechTalk #51 Behind the sceene a RECOMMENDER SYSTEM 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
35 http://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 food food has characteristic of taste (measure by level) : - sweet - bitter - savory - salty - sour - spicy - sauce - meat - vegetable
user item • previous taste preference a model... • rating • comment • comment • current location • Restoran => foods feedback recommended item - Restoran with foods that meet user taste preferences
end

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