2. E-Commerce
Collaborative filtering is a method of making
automatic predictions (filtering) about the
interests of a user by collecting preferences or
taste information from many users
(collaborating). from wiki
3. when we choose one book,amazon will recommend
other book we maybe like
5. 1. Weight all users with respect to similarity with active user
2. Select a subset of users to use as a set of predictors
3. Compute a prediction from a weighted combination of selected
neighbors’ ratings
6. 1. Weight all users with respect to similarity with active user
simple
compute
Nathan [5,1,5]
Joe [5,2,5]
John [2,5,2.5]
Al [2,2,4]
use cosine compute similarity
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 use as a set of predictors
if there are hundreds of user,
we can choose the higher similarity
choose n of m(sum of user is m)
8. 1. Weight all users with respect to similarity with active user
cos (Nathan,Joe) 0.99
cos (Nathan,John) 0.64
cos (Nathan,Al) 0.91 ? = 3.03
2. Select a subset of users to use as a set of predictors
3. Compute a prediction from a weighted combination of
selected neighbors’ ratings
(0.99*4+0.64*3+0.91*2)
(0.99+0.64+0.91)
0.99
0.64
0.91
9. ✤ User-Based CF
✤ Item-Based CF
compute similarity base on user
compute similarity base on item
10. ✤ User-Based CF
compute similarity base on user
if predict user A to item4 rating
user B to item4 rating is 5
user F to item4 rating is 1
user A to item4 =
5 * similarities (user A, user B) + 1 * similarities (user A, user F)
similarities (user A, user B) + similarities (user A, user F)
11. ✤ Item-Based CF
compute similarity base on item
if predict user A to item4 rating
user A to item2 rating is 1
user A to item3 rating is 2user A to item4 =
1 * similarities (item2, item4) + 2 * similarities (item3, item4)
similarities (item2, item4) + similarities (item3, item4)
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
Pearson Correlation Similarity
15. what is different between
?
Pearson Correlation SimilarityCosine Similarity
AWS:
lower user bias!
16. what is different between
Pearson Correlation Similarity
Cosine Similarity Adjusted Cosine Similarity
advanced
average user rating
average item rating
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 use as a set of predictors
3. Compute a prediction from a weighted combination of selected
neighbors’ ratings
choose n of m(sum of user is m)
19. Collaborative Filtering problem ?
Cold-start
Sparsity
Scalability
ALS-Alternating Least Squares
SVD-singular value decomposition
Hybrid Recommendation Systems
Scaling-Up Item-Based Collaborative Filtering
Recommendation Algorithm Based on Hadoop
20. Collaborative Filtering Gist
Collaborative Filtering ipynb online
Scaling-up Item-based Collaborative Filtering
Recommendation Algorithm based on Hadoop PPT
code and PPT