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1. Collaborative Filtering
is a technique usedby somerecommender systems
NCKU-hpdsTienYang

2. E-Commerce
C
ollaborativefilteringisa methodof
making automatic predictions (filtering)
about the
interests of a user by collecting preferences or
taste information from many users
(collaborat ing). from wiki

3. when we chooseonebook，amazon will
recommend other book we maybelike

4. how amazon know what I like?

5. 1.Weight all users with respect to similarity with active user
2. Select a subset of users to useas a set of predictors
3
.
C
omputea prediction f roma weightedcombination of selected
neighbors’ ratings

6. 1. Weight all users with respect to similarity with active user
simple
compute
Joe[5,2,5]
John [2,5,2.5]
Al [2,2,4]
Nathan [5,1,5]
usecosinecomputesimilarit y
cos(Nathan,Joe) 0.99
cos(Nathan,John) 0.64
cos(Nathan,Al) 0.91

7. 1. Weight all users with respect to similarity with active user
2. Select a subset of users to useas a set of predictors
if there are hundredsof user,
we can choose thehigher similarit y
choosen of m(sumof user ism)

8. cos(Nathan,Joe) 0.99
cos(Nathan,John) 0.64
cos(Nathan,Al) 0.91
(0.99*4+0.64*3+0.91*2)
(0.99+0.64+0.91)
?=3.03
0.99
0.64
0.91
1.Weight all users with respect to similarity with active user
2. Select a subset of users to useas a set of predictors
3
.
C
omputea predict ion f roma weightedcombinat ion of
selectedneighbors’ rat ings

9. ✤ User-BasedC
F
✤ Item-BasedC
F
computesimilarit y baseon user
computesimilarit y baseon item

10. ✤ User-BasedC
F
computesimilarit y baseon user
if predict user A toitem4 rating
user Btoitem4 ratingis5
user Ftoitem4 ratingis1
user A toitem4 =
5 * similarities(user A, user B
) +1 * similarities(user A, user F)
similarities(user A, user B
) +similarities(user A, user F)

11. ✤ Item-BasedC
F
computesimilarit y baseon item
if predict user A toitem4 rating
user A toitem2 ratingis1
user A toitem3 ratingis2
user A toitem4 =
1 * similarities(item2, item4) +2 * similarities(item3, item4)
similarities(item2, item4) +similarities(item3, item4)

12. similarity!?
C
osineSimilarit y
(1,-1)
how about
P
earson C
orrelat ion Similarit y
?

13. j
Covariance
P
earson C
orrelat ion Similarit y
(1,-1)

14. apple milk toast
sam 2 0 4
john 5 5 3
tim 2 4 ?
u
i
j
Ri =(2+5)/2 Rj =(4+3)/2
P
earson C
orrelat ion Similarit y

15. what isdif ferent bet ween
?
P
earson C
orrelation Similarit y
C
osineSimilarit y
AWS:
lower user bias!

16. what isdif ferent bet ween
C
osineSimilarit y AdjustedC
osineSimilarit y
P
earson C
orrelation Similarit y
advanced
averageuser rat ing
averageitemrat ing

17. apple milk toast
sam 2 0 4
john 5 5 3
tim 2 4 ?
u
i
j
?=
2 * similarities(apple, toast) +4 * similarities(milk, toast)
similarities(apple, toast) +similarities(milk, toast)

18. so
1. Weight all items with respect to similarity with active items
2. Select a subset of items to useas a set of predictors
3
.
C
omputea prediction f roma weightedcombination of selected
neighbors’ ratings
choosen of m(sumof user ism)

19. Cold-start
C
ollaborat iveFilteringproblem?
SVD-singular value decomposition
Sparsity
ALS-Alternat ingLeast Squares
HybridRecommendat ion Systems
Scalability
Scaling-Up Item-Based Collaborative Filtering
Recommendat ion AlgorithmBasedon Hadoop

20. Collaborative Filtering Gist
Collaborative Filtering ipynb online
Scaling-up Item-based Collaborative Filtering
Recommendat ion Algorithmbasedon HadoopPPT
codeandPPT

21. reference
Item-based collaborative filtering Algorithm
Collaborative filtering wiki
P
earson correlat ion coefficient wiki
協同過濾法 (collaborat iveFiltering) 及相關概念