Authors: Sofiane Abbar, Habibur Rahman, Saravanan Thirumuruganathan, Carlos Castillo, Gautam Das Organization: Qatar Computing Research Institute (卡達,阿拉伯半島中的國家) University of Texas at Arlington IEEE International Conference on Data Engineering, 2014

1. Reading Paper
by Oscar Li Jen Hsu
2015/05/28
SCOPE Lab
National Tsin Hua University

2. Paper title:
Ranking Item Features by Mining
Online User-Item Interactions
 Authors:
 Sofiane Abbar, Habibur Rahman, Saravanan
Thirumuruganathan, Carlos Castillo, Gautam Das
 Organization:
 Qatar Computing Research Institute (卡達,阿拉伯半島中的國家)
 University of Texas at Arlington
 IEEE International Conference on Data Engineering, 2014

3. User-Item Interaction Matrix

100
101
80
82
1
101
2
82
 Row , item
 Column, user
 User_1 visits item_1 100 times, visits item_2 1
times.
 V : User-Item Interaction Matrix
 v : for single user

4. Features
 Features like “movie actor, Tom Hanks“, “movie
actor, Catherine Zeta-Jones”
 Each element in feature matrix is a probability.
 By the method with features, we can predict user
preference on new items.

5. Database with 3 features and 2
items.
 To be given
Features Items Users

6. Terminology
 H: User-Feature Preferences Matrix
 h: for single user
 W: Feature-Item Transition Matrix
 Each element is a probability.
 𝑊: Boolean feature-item matrix
ℎ =
100
202
101
202
1
202
𝑊 =
1 0.5 0
0 0.5 0.1
𝑊 =
1 1 0
0 1 1

7. User Interactions Model
 𝑊ℎ = 𝑣
 The most important case for me is that h is
unknown.
 “𝑊ℎ” is not always equal to “v”.

8. Least Square Error
 min 𝑣 − 𝑊ℎ 2
 𝑊ℎ ~ 𝑣

9. TO SOLVE UNKNOWN
VARIABLE IN TWO CASES

10. Case 1
 Given: v,W
 Getting: h
 To solve linear equations
 Least Square Error: 𝑣 − 𝑊ℎ 2
 Singular Value Decomposition
 𝑊ℎ = 𝑣 →

11. Case 2
 Given: 𝑊, ℎ, 𝑣
 Getting: W
 Maximum Network Flow method
 𝑂 𝑛𝑚

𝑊𝑖𝑎 𝑊𝑖𝑏 0
0 𝑊𝑗𝑏 𝑊𝑗𝑐
100
202
101
202
1
202
=
100
101
1
101