1. Advisor: Prof. Shou-de Lin (林守德) Student: Eric L. Lee (李揚)
Collaborative Filtering Based Model for Privacy-Preserving Course Recommendation
Advisor: Prof. Shou-de Lin (林守德)
Student: Eric L. Lee (李揚)

2. Outline Motivation and Introduction Baselines Our Methods
Experiment and Results
Conclusion

3. Motivation
There are courses for students to choose in 2012, it costs lots of time for students searching courses they want to take!!
We aims to develop a course recommendation system using only course records to protect the privacy of users in this paper.

4. Example My Model Recommended list Machine Learning Statistics Stu Cou…
Most
recommend
Stu
Cou
Time
Calculus
2009-1
Statistics
2010-2 1
Probability
2010-1 2
Physics
2008-2 .. …
My Model
Least
recommend
Former and current Students’ Course Record
Output recommended list for each current students

5. Related Work Collaborative Filtering [1] [2] [3] (our baselines)
Privacy-Preserving Course Recommendation
Our Work!!!
[1] Deshpande, Mukund, and George Karypis. "Item-based top-n recommendation algorithms." ACM Transactions on Information Systems (TOIS) 22.1 (2004):
[2] Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, (8),
[3] Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009, June). BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (pp ). AUAI Press.

6. One Class Collaborative Filtering Problem1 ? 5 ? 3 2 4 1
Vs.
OCCF problem
Traditional Recommendation Problem

7. Lack of positive training data in the next grade
1st ~3rd grade
4th grade
Former students 
Current Students
Current students we want to predict don’t have training data in the 4th grade.

8. Courses’ Order Traditional CF assume each course actions are i.i.d
However, we think the course records in the consecutive year have a closer relationship.

9. Our Framework P1 P2 P3 MF Matrix Factorization (MF) Memory-Based CF P2
Bayesian Personal Ranking (BPR)
Reduce Search Space P2
Two Stage Method
P1: OCCF
P2: Lack of training data in next grade
P3: courses’ order P3
Course Network Regularization

10. Modified Memory Based CF
Origin CF
Modified CF
Search space for similar students
All other students
Only Former students
Course Candidates
All the courses taken by the compared students
Only 4th grade courses taken by the compared students

11. Modified Memory-Based Collaborative Filtering
1st ~ 3rd grade
4th grade
Former
Students
Calculate the similarities
Current
Students
Student u’s course record
𝑠∈𝑓𝑜𝑟𝑚𝑒𝑟 𝑠𝑡𝑢𝑑𝑒𝑛𝑡𝑠 𝑠𝑖𝑚(𝑢,𝑠)
Score(u, 𝑐) ≔
∗𝑝𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒(𝑠,𝑐)

12. Our Framework P1 P2 P3 Matrix Factorization (MF)
Bayesian Personal Ranking (BPR)
P1: OCCF
P2: Lack of training data in next grade
P3 : courses’ order P2
Two Stage Method P3
Course Network Regularization

13. MF Minimizing Square Error2 ? 1 3 4 ≈ X
Q (courses) R
P (student)
min P,𝑄 𝑢,𝑖 ∈𝑟𝑎𝑡𝑖𝑛𝑔𝑠 [ ( 𝑅 𝑢,𝑖 − 𝑃 𝑢 ∙ 𝑄 𝑖 ) 2 ]+ 𝐶( 𝑃 2 + |𝑄| 2 )
Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 8 (2009):

14. Our Framework P1 P2 P3 Matrix Factorization (MF)
Bayesian Personal Ranking (BPR) P2
Two Stage Method
P1: OCCF
P2: Lack of training data in next grade
P3: Courses’ order P3
Course Network Regularization

15. One class collaborative Filtering (OCCF) Problem
Course 1
Course 2
Course 3
Course 4
Student 1 ? 1
Student 2
Student 3
Student 4
Characteristics: The rating matrix only has one class(take / or not take the course).

16. Bayesian Personal Ranking : Intuition
student u
𝑗∈𝐼 𝑢 − 
𝑖∈𝐼 𝑢 +
 …
… 
Maximize the likelihood!!!
𝐼 𝑢 + :𝑇ℎ𝑒 𝑐𝑜𝑢𝑟𝑠𝑒𝑠 𝑡ℎ𝑎𝑡 𝑡ℎ𝑒 𝑠𝑡𝑢𝑑𝑒𝑛𝑡𝑠 ℎ𝑎𝑣𝑒 𝑡𝑎𝑘𝑒𝑛
𝐼 𝑢 − :𝑇ℎ𝑒 𝑐𝑜𝑢𝑟𝑠𝑒𝑠 𝑡ℎ𝑎𝑡 𝑡ℎ𝑒 𝑠𝑡𝑢𝑑𝑒𝑛𝑡𝑠 ℎ𝑎𝑣𝑒 𝑛 ′ 𝑡 𝑡𝑎𝑘𝑒𝑛

17. Bayesian Personal Ranking: Intuition
We want to maximize the AUC that + rank higher than ?
Let 𝐷 𝑆 ≔ 𝑢,𝑖,𝑗 |(𝑖,𝑗)∈ 𝐼 𝑢 + × 𝐼 𝑢 −
max 𝜃 (𝑢,𝑖,𝑗)∈ 𝐷 𝑠 𝑃𝑟(𝜃| 𝑅 𝑢𝑖 > 𝑅 𝑢𝑗 )
m𝑖𝑛 𝜃 ⁡ (𝑢,𝑖,𝑗)∈ 𝐷 𝑠 ln⁡(1+ 𝑒 −(𝑦 𝑢𝑖 𝜃 − 𝑦 𝑢𝑗 (𝜃)) ) + ?
Rendle, Steffen, et al. "BPR: Bayesian personalized ranking from implicit feedback." Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2009.

18. Matrix Factorization using Bayesian Personal Ranking (BPRMF)≈ X
Rating Matrix P Q
𝑢,𝑖,𝑗 ln 1+ exp − 𝑃 𝑢 𝑄 𝑖 − 𝑄 𝑗 C 1 P + C 2 Q

19. Our Framework P1 P2 P3 Matrix Factorization (MF)
Bayesian Personal Ranking (BPR)
P1: OCCF
P2: Lack of training data in next grade
P3: Model the course relation P2
Two Stage Method P3
Course Network Regularization

20. Recall of BPRMF Bayesian Personal Ranking   … … 
student u
 …
…
𝐼 𝑢 +
𝐼 𝑢 −  
What courses has been taken by student u
Who take the course c ?
Course c latent features
Student u latent features

21.  Problems Lack of training data in the next grade.
1st ~3rd grade
4th grade
Former students 
Current Students
Lack of training data in the next grade.
Therefore, these courses are all in the set of 𝐼 𝑢 − for current students

22. Problems 
Courses in 1st ~3rd grade
Student u 
Courses in 4th grade
Because the students u doesn’t have the positive training data in 4th grade, the final model tends to have an unreasonable bias to rank courses in 4th grade lower.

23. Two Stage Method - Motivation
Students
The latent features of course A
The latent features of course B
The latent features of two courses would be similar if they are taken by the same group of students

24. Two Stage Method - Motivation
Although BPRMF have bias on 4th grade courses, it still learn the type of courses.
If a student often take certain type of courses in the 1st ~ 3rd grade, he may take the same type of courses in the 4th grade.

25. Two Stage Method - Intuition
Learn the type for courses
Rank the type of courses the current students like
Notation:
P => The latent matrix for students
Q => The latent matrix for courses

26. First Stage P Q min 𝑃,𝑄 𝑢,𝑖,𝑗 ln 1+ exp − 𝑃 𝑢 𝑄 𝑖 − 𝑄 𝑗 +𝐶|𝑃|
Former students
Get the Courses’ latent features
Current Students P Q
min 𝑃,𝑄 𝑢,𝑖,𝑗 ln 1+ exp − 𝑃 𝑢 𝑄 𝑖 − 𝑄 𝑗 𝐶|𝑃|

27. Second Stage Q P min 𝑃 𝑢,𝑖,𝑗 ln 1+ exp − 𝑃 𝑢 𝑄 𝑖 − 𝑄 𝑗 +𝐶|𝑃|
Former students
Get the current students’ latent feature
Current students Q P
min 𝑃 𝑢,𝑖,𝑗 ln 1+ exp − 𝑃 𝑢 𝑄 𝑖 − 𝑄 𝑗 𝐶|𝑃|

28. Review of Two Stage MethodsX
Use all the training course records Q P
Use the course records of current students only X P Q

29. Our Framework P1 P2 P3 Matrix Factorization (MF)
Bayesian Personal Ranking (BPR)
P1: OCCF
P2: Lack of training data in next grade
P3: Courses’ Order P2
Two Stage Method P3
Course Network Regularization

30. Review of Two Stage Methods
First Stage
Learn the type of courses
Second Stage
Rank what the type of courses the students like to take

31. Idea
Observation: Some courses will be very likely to be taken after certain courses in a consecutive year. And the Courses are often belongs to the same type
Idea: Let these courses have similar course latent features

32. Recommender with social regularization
Idea : The behaviors of the neighbors might be similar.
More likely to happen
Less likely to happen

33. Use the regularization term to let the latent features similar
Individual based regularization
𝛽 2 𝑖 ∈𝑛𝑜𝑑𝑒𝑠 𝑓∈𝑁(𝑖) 𝑙𝑖𝑛𝑘(𝑖,𝑓)|| 𝑄 𝑖 − 𝑄 𝑓 || 2
Where N(i) is the set of neighbors of node i
link(i,f) is the weight between node i and node f
Ma, Hao, et al. "Recommender systems with social regularization." Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011.

34. Objective Function
𝑢,𝑖,𝑗 ln 1+ exp − 𝑃 𝑢 𝑄 𝑖 − 𝑄 𝑗 C 1 P + C 2 Q + β 2 𝑖 𝑁 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 (𝑖) | 𝑄 𝑖 − 𝑄 𝑗 | 2 ∗F𝑜𝑙𝑙𝑜𝑤(𝑖,𝑗)
𝑾𝒉𝒆𝒓𝒆 𝒇𝒐𝒍𝒍𝒐𝒘 𝒊,𝒋 𝒊𝒔 𝒕𝒉𝒆 𝒘𝒆𝒊𝒈𝒉𝒕 𝒐𝒇 𝒕𝒉𝒆 𝒍𝒊𝒏𝒌 𝒃𝒆𝒕𝒘𝒆𝒆𝒏 𝒊 𝒂𝒏𝒅 𝒋
𝑾𝒉𝒆𝒓𝒆 𝑵 𝒕𝒉𝒓𝒆𝒔𝒉𝒐𝒍𝒅 𝒊 𝒊𝒔 𝒂 𝒔𝒆𝒕 𝒕𝒉𝒂𝒕 𝒇𝒐𝒍𝒍𝒐𝒘 𝒊,𝒋 >𝒕𝒉𝒓𝒆𝒔𝒉𝒐𝒍𝒅
Non Prunning
Prunning

35. Example Name 1st grade 2nd grade 3rd grade Alice AB C D Bob A CD B
Christine AD
Follow (A,B) =
# B courses taken after courses A in one year # courses taken after courses A in one year
= 1 4
=0.25

36. Final Course Network of the simple small university
Name
1st grade
2nd grade
3rd grade
Alice AB C D
Bob A CD B
Christine AD

37. Combining Social Network to MF model
Pruning ≈ X

38. Stochastic Gradient Descent
Target Function:
𝑢,𝑖,𝑗 ∈ 𝐷 𝑠 ln 1+ exp − 𝑃 𝑢 𝑄 𝑖 − 𝑄 𝑗 C 1 P 2 + C 2 Q β 2 𝑖 𝑁 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 (𝑖) | 𝑄 𝑖 − 𝑄 𝑗 | 2 ∗𝑓𝑜𝑙𝑙𝑜𝑤(𝑖,𝑗)
For each update:
𝑠𝑖𝑔= − 𝑒 − 𝑘 𝑃 𝑢𝑘 ( 𝑄 𝑖𝑘 − 𝑄 𝑗𝑘 ) 1+ 𝑒 − 𝑘 𝑃 𝑢𝑘 ( 𝑄 𝑖𝑘 − 𝑄 𝑗𝑘 )
𝑃 𝑢 ≔ 𝑃 𝑢 −𝑎𝑙𝑝ℎ𝑎 ∗(𝑠𝑖𝑔∗ 𝑄 𝑖𝑘 − 𝑄 𝑗𝑘 + 𝐶 1 𝑃 𝑢 )
𝑄 𝑖 ≔ 𝑄 𝑖 −𝑎𝑙𝑝ℎ𝑎∗(𝑠𝑖𝑔 ∗ 𝑃 𝑢 + 𝐶 2 𝑄 𝑖 + 𝛽 𝑛𝜖 𝑁 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 (𝑖) 𝑄 𝑖 − 𝑄 𝑛 ∗𝑓𝑜𝑙𝑙𝑜𝑤(𝑖,𝑛) )
𝑄 𝑗 ≔ 𝑄 𝑗 −𝑎𝑙𝑝ℎ𝑎∗(𝑠𝑖𝑔 ∗( −𝑃 𝑢 )+ 𝐶 2 𝑄 𝑖 + 𝛽 𝑛𝜖 𝑁 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 (𝑖) 𝑄 𝑗 − 𝑄 𝑛 ∗𝑓𝑜𝑙𝑙𝑜𝑤(𝑗,𝑛) )

39. Personalized page rank
Parameters: 𝛼 , T(the set of courses the student has taken): Assuming the student has taken C, D and E courses previously
Choose a course from the neighbors with probability proportional to the weight of the link A B
𝑃(𝛼) E C D
𝑃(1−𝛼)
Randomly choose a course from set T with a uniform distribution

40. Overview of our Methods
CF- based model
Stu
Cou
Time 2
2011-1 3
2012-2 1 4
2011-2 .. …
Apply BPR
Two Stage Method
Social Regularization
Perform personalized page rank
Construct the course networks
Graph-based model

41. Ensemble
CF-Based Model
Linear RankSVM
Graph-Based Model

42. Data Set Description Use the triplets
<student_id, course_id, Time(Year-Semester)>
Year
# Students
# Course Actions
民國97
4736
311283
民國98
4686
299772
民國99
4555
285561

43. Training, Validation and Testing set
1~3 grade
4th grade
Use the students in the fourth grade in 民國98 as validation set to choose the parameters
民國97
民國98
Test our model use the course records of fourth grade students enter NTU in 民國99
民國99

44. Evaluation
The average AUC (Area under Reciever Opterating Characteristics) of students
True positive Rate
Ranking of the courses
XOXXOOOXXOOOXXO
X: the student doesn’t take the course
O: the student does take the course
False Positve Rate

45. Memory Based Collaborative Filtering
Let 𝐴 𝑠 be the set of courses of student a take in s semesters
Let 𝐵 𝑠 be the set of courses of student b take in s semesters
Number of Intersection
𝑠𝑖𝑚(𝑎,𝑏)= 𝑠∈𝑠𝑒𝑚𝑒𝑠𝑡𝑒𝑟𝑠 𝐴 𝑠 ∩ 𝐵 𝑠
Jaccard Similarity
sim(a,b)= 𝑠∈𝑠𝑒𝑚𝑒𝑠𝑡𝑒𝑟𝑠 𝐴 𝑠 ∩ 𝐵 𝑠 / 𝐴 𝑠 ∪ 𝐵 𝑠
TFIDF similarity
𝑠𝑖𝑚(𝑎,𝑏)= 𝑠∈𝑠𝑒𝑚𝑒𝑠𝑡𝑒𝑟𝑠 𝑐∈ 𝐴 𝑠 ∩ 𝐵 𝑠 𝑇𝐹𝐼𝐷𝐹(𝑐)

46. Memory-based CF Methods AUC Most Popular 0.817208 Normal CF 0.744253
Number of Intersection
Jaccard
TFIDF
TFIDF_Whole
(X以偷看到testing)
Jaccard_Whole

47. Result of Model-Based Model
Methods
AUC
One Stage BPRMF
Two Stage BPRMF
Two Stage BPRMF with Course Network
P-value
Target
Baseline
P-value
Two Stage BPRMF
One Stage BPRMF
𝟑.𝟐𝟒 ∗ 𝟏𝟎 −𝟏𝟐
Two Stage BPRMF with Course Network
𝟎.𝟎𝟏𝟑𝟒𝟓𝟔

48. Personalized Pagerank and Ensemble
Methods
AUC
Personalized Page Rank
Ensemble

49. Result – AUC with 95% confident interval
Methods
AUC with 95% confident interval
Most Popular ±
Normal CF ±
Number of Intersection Similarity ±
Jaccard Similarity ±
TFIDF Similarity ±
Personalized Page Rank ±
BPRMF (one stage) ±
BPRMF (two stage) ±
BPRMF with course network prior (one stage) ±
BPRMF with course network prior (two stage) ±
Ensemble
±𝟎.𝟎𝟎𝟏𝟎𝟏𝟑

50. Conclusion and Future work
We propose an accurate privacy-preserving course recommendation system.
We can think the course record as a recommendation problem with seasonal product. Our method has the potential to apply on these kinds of data sets.

51. Thank you for listening