Using LSH for predicting user ratings on the items.

1. LSH
for
Prediction Problem
in Recommendation
Maruf Aytekin
PhD Student
Computer Engineering Department
Bahcesehir University
May 5, 2015

2. Outline
• User-based
• Item-based
• LSH
• Parameters
• Model Build Performance
• Accuracy Performance
• LSH Parameters

3. Data Set
Total Ratings: 100000
Number of Users : 943
Number of Items : 1682
Sparsity = 0.0630

4. Evaluation Methods
• We use hold out cross validation methot for the
experiments
• We select %5 for test %5 for
validation data randomly.
• Repeat this process 3 times and
averaged out the results

5. User-based
Neighbors can have different levels of similarity.
Wuv: Similarity of user u and v.
rvi: Rating value of user v for item i.
Ni(u): Set of neighbors who have rated for item i.

6. ruj: Rating value of user u for item j.
Nu(i): the items rated by user u most similar to item i.
Wij: Similarity of item i and j
Item-based

7. U1
U2
U3
Um
.
.
.
.
.
H1
H2
U7
U11
U10
.
.
U13
U39
Um
.
.
U1
U3
U9
.
.
U2
U5
U6
.
.
bucket 1
key: 0101
bucket 2
key: 1110
bucket 3
key: 1101
bucket 4
key: 1001
[0,1]
[0,1]
AND-Construction
Locality Sensitive Hashing

8. Hash Tables
U2
U6
U1
U3
.
.
.
candidate set for U5:
C(U5)
L = 2
K = 4
t = 1
t = 2

9. LSH for Prediction
L : number of hash tables (bands)
Cvi(t) : the set of candidate pairs retrieved from hash table t
rated for item i.
rvi : rating of user v (in C) on item i

10. Computational Complexty
|U | : User set size
| I | : Item set size
k : Number of neighbors used in the predictions
p : Maximum number of ratings per user
q : Maximum number of ratings per item

11. Parameters (CF)

12. LSH Parameters

13. LSH Parameters

14. Model Build Time

15. Results
User-based
With the optimum k = 30 and Y=7 ;
• Average MAE: 0.79527
• Average running time: 9.437 seconds.
We compare this results LSH method.

16. LSH & User-based
Hash Functions

17. LSH & User-based
Hash Functions

18. LSH & User-based
Hash Tables

19. LSH & User-based
Hash Tables

20. Conclusion
• LSH tremendously improved the scalability
• Accuracy decreased in acceptable ranges
• Performance improved a lot.
• LSH needs to be configured to balance MAE and
performance according to expectations from the
system.

21. Source Code
User-based Prediction:

22. Source Code
LSH Prediction:

23. Q&A