1. Ahlia University
College of Information Technology
By:
Khadija Atiya Al-Mohsen 201110493
Supervised by:
Dr. Huda AlJobori

2. • Overview of Big Data
– 6 Vs of Big Data
– Big Data Technologies
• Recommender system
– Definition
– Problem Formulation
– Approaches; CF in particular
– Challenges of CF
– Singular Value Decomposition (SVD) approach
• My Experiments
• Conclusion
• Future work

3. Stage 1

4. Stage 2

5. Stage 3

6. olume•

7. olume
elocity
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8. ariety•
olume
elocity
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9. eracity•
ariety•
olume
elocity
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10. ariability•
eracity•
ariety•
olume
elocity
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11. • alue
ariability•
eracity•
ariety•
olume
elocity
•
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13. • Recommender System (RS)
– For every users, RS Suggests new items that might
be of interest to the user
– It analyzes the users purchase history , their
profiles and their activities on the website

14. ??

15. • Collaborative Filtering (CF) RS
– Assumption:
• Users may have similar opinions
• User opinion is stable
– 2 Variation:
• User-based RS
• Item-based RS

16. • Collaborative Filtering (CF) RS
• User-based RS
• Item-based RS
1 2 3 4 5
1    
2 
3  
users movies

17. • Scalability
• Data Sparsity
• Synonymy
• Cold Start
• Grey sheep

18. R = U S VT
mxn mxm mxn nxn
. .
U: holds left singular vectors of 𝑅 in its columns
S: holds the singular values of 𝑅 in its diagonal entries in decreasing order
V: holds the right singular vectors of 𝑅 in its columns

19. R = S
mxn mxm mxn nxn
. .
U: holds users features
S: holds the features
V: holds movie features
User-item matrix User feature item featurefeatures
users
movies
…………..
……comedy
horror
romantic
comedy
horror
romantic

20. R
≈
U S VT
mxn mxk kxk kxn
. .
=

21. R ≈ U S
VT
mxn mxk
kxk kxn
. .
User-item matrix User feature
features item feature
?

22. • Preprocessing:
– Imputation:
• By zero (ByZero)
• By item average rating (ItemAvgRating)
• By user average rating (UserAvgRating)
• By mean of ItemAvgRating and UserAvgRating
(Mean_ItemAvgRating&UserAvgRating)
– Normalization
• Offsetting the difference in rating scale between the users
• Post-processing:
-De-normalization

23. • SVD-based RS:
– Researches started in 2000.
– Data size started to increase.
– Good results: accuracy, speed and scalability
– Incremental SVD, combining it with neural network,
with traditional CF approaches
– Tested on Big Data : relatively small data set
• Questions:
– No longer valid for TODAY BIG DATA?
• Appropriate value of k
• Appropriate imputation technique
– New Big Data tools and frameworks (Hadoop & Spark)?

24. • Experimental Environment:
– MacBook Pro with X 10.9.3 OS
– Apache Hadoop 2.4.0
– Apache Spark 1.0.2.
• Data Set:
– 1M MovieLense set
– Divided into training set and test set
• Training ratio x
• Evaluation Metric
– Mean Absolute error (MAE)

25. • Experimental steps:
– Data set  Hadoop
– Construct user-item matrix
– Several Experiments: Imputation by
Mean_ItemAvgRating&UserAvgRating
– Normalization
– Compute SVD using Apache Spark
• Several Experiments : K = 20
• Result: Uk, Sk and Vt
k
– Evaluation:
• Multiply Uk, Sk and Vt
k == Predictions
• Compute MAE between test set and the predictions

27. • Determining the appropriate number of
dimensions
Training Ratio
k x= 40% x= 60% x= 80%
10 0.752063387 0.740626631 0.729999025
20 0.751235952 0.737954414 0.724461829
30 0.751605786 0.738107001 0.723582636
40 0.752364535 0.738714968 0.723803209
50 0.753327523 0.739710017 0.725192112
60 0.754025958 0.740751258 0.726154802
70 0.754724263 0.741471676 0.727282644
80 0.755378978 0.742729653 0.728420107
90 0.755930363 0.743485881 0.729623117
100 0.756510921 0.744293948 0.730338578
150 0.759248743 0.748479721 0.735940524
200 0.761302063 0.752022533 0.740869085
250 0.762834111 0.754352648 0.744583552
300 0.764142161 0.756565002 0.747871699

28. 0.72
0.725
0.73
0.735
0.74
0.745
0.75
0.755
0.76
0.765
0.77
0 50 100 150 200 250 300 350
MAE
number of dimension k
SVD prediction quality variation with number of dimensions k
x= 40%
x= 80%
x= 60%

29. • Determining the best imputation technique
Imputation Technique
k ByZero UserAvgRating ItemAvgRating Mean_ UserAvgRating&ItemAvgRating
10 1.287082589 0.763900832 0.738045498 0.729999025
20 1.340593039 0.758800163 0.732242767 0.724461829
30 1.351316283 0.758681267 0.731619885 0.723582636
40 1.342134276 0.760268678 0.731981172 0.723803209
50 1.331709429 0.762213489 0.733055102 0.725192112
60 1.317791963 0.764543289 0.733818382 0.726154802
70 1.303016117 0.766207466 0.734838138 0.727282644
80 1.288611141 0.768863197 0.736044939 0.728420107
90 1.274236198 0.770501176 0.737138793 0.735940524
100 1.261225796 0.772165966 0.738514469 0.730338578
150 1.198043117 0.780474068 0.744252067 0.735940524

30. 0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
0 20 40 60 80 100 120 140 160
MAE
number of dimension k
Comparing the MAE of different imputation technique
ByZero
UserAvgRating
ItemAvgRating
Mean_ UserAvgRating&ItemAvgRating

31. • Evaluating the accuracy of SVD on different
training ratio
k
Training
Ratio x
k=20 k=100
0.35 0.7549804 0.759679233
0.4 0.751235952 0.756510921
0.45 0.747814989 0.753365509
0.5 0.744018997 0.749880733
0.55 0.741011898 0.747432557
0.6 0.737954414 0.744293948
0.65 0.734098595 0.740644033
0.7 0.731331182 0.737610654
0.75 0.727741033 0.734113758
0.8 0.724461829 0.730338578
0.85 0.721460479 0.727461993
0.9 0.72096045 0.726117655
0.95 0.733633592 0.740065812

32. 0.715
0.72
0.725
0.73
0.735
0.74
0.745
0.75
0.755
0.76
0.765
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
MAE
training ratio x
SVD prediction quality variation with different training ratio x
k=20
k=100

33. • Results and Discussion:
 Easy parallelization
Powerfulness of Hadoop and spark
Comparable results with previous work
K = 20 (K = 14, k =15)
MAE between 0.724 and 0.730 (0.732 and 0.748)
Importance of preprocessing steps
Sensitivity of prediction quality to data sparsity
Applicability of SVD for Today Big Data

34. • Big Data  challenges on RS
– Sparsness of the data
– Scalability
– Prediction quality
• Goals of RS in Big Data Era
– Process million of records + good predictions
• SVD is a promising CF approach
• New tools and frameworks are useful in building
large scale system
• Carried on experiments : SVD + Apache Hadoop
and spark
.
.
.

35. • A careful implementation of a scalable SVD-
based collaborative filtering recommender
system is effective when choosing the right
parameters and the appropriate frameworks
and APIs.

36. • Deploying it on multi-node cluster
• Incremental SVD/ SSVD
• Expectation- Maximization approach
• A hybrid of these approaches
• Compare Spark implementation with other APIs
and tools like Apache Mahout.