Identification of Grey Sheep Users By Histogram Intersection In Recommender Systems

1. Yong Zheng, Mayur Agnani, Mili Singh
Illinois Institute of Technology
Chicago, IL, 60616, USA
2017 International Conference on Advanced Data Mining and
Applications, Singapore, Nov 5-6, 2017
Identification of Grey Sheep Users By Histogram
Intersection In Recommender Systems

2. Agenda
• Background: Recommender Systems
• Grey Sheep Users In Collaborative Filtering
• Methodology and Solutions
• Experimental Results
• Conclusions and Future Work
2

3. Recommender System (RS)
• RS: item recommendations tailored to user tastes
3

4. Traditional Recommendation Algorithms
4
Content-Based Recommendation Algorithms
The user will be recommended items similar to the ones the
user preferred in the past, such as book/movie recsys
Collaborative Filtering Based Recommendation Algorithms
The user will be recommended items that people with similar
tastes and preferences liked in the past, e.g., movie recsys
Hybrid Recommendation Algorithms
Combine content-based and collaborative filtering based
algorithms to produce item recommendations.

5. Collaborative Filtering: Algorithms
5
User-Based KNN Collaborative Filtering (UBCF)
Assumption: a user u’s rating on item t is similar to
other users’ rating on item t, while this group of
similar users is called user K-nearest neighbor
Pirates of the
Caribbean 4
Kung Fu Panda 2 Harry Potter 6 Harry Potter 7
U1 4 4 1 2
U2 3 4 2 1
U3 2 2 4 4
U4 4 4 1 ?

6. Collaborative Filtering: Algorithms
6
User-Based KNN Collaborative Filtering (UBCF)
Pirates of the
Caribbean 4
Kung Fu Panda 2 Harry Potter 6 Harry Potter 7
U1 4 4 1 2
U2 3 4 2 1
U3 2 2 4 4
U4 4 4 1 ?
a = the target user
i = the target item
N = user neighborhood
u = a user neighbor in N

7. Collaborative Filtering: Algorithms
7
Popular Challenges in Collaborative Filtering
Data sparsity problems
Cold-start users or items
Grey-sheep users
Incorporate content into collaborative filtering
….

8. Grey Sheep Users
8
Definition 1 by Mark Claypool, et al., 1999
A group of users who neither agree nor disagree with any group
of users. Therefore, they will not benefit from the user-based
collaborative filtering technique
Clustering Technique by Ghazanfar, et al., 2011
Distribution of User Ratings by Gras, et al., 2016
Definition 2 by John McCrae, et al., 2004
White Sheep Users may have high correlations with other users;
Black Sheep Users have very few or no correlating users; Grey
Sheep Users own unusual tastes and low correlations with others
Distribution of User Similarities by Zheng, et al., 2017

9. Research Problem: Identifying Grey Sheep Users
9
Collaborative Filtering Other Algorithms

10. Proposed Solution
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Approach Based on The Distribution of User Similarities
White Sheep Users: high correlations with other users
Black Sheep Users: very few or no correlating users
Grey Sheep Users: unusual tastes, low correlations with others
The Distribution of User-User Correlations or Similarities

11. Proposed Solution
11
Proposed Solution
Step 1, represent each user as distribution of user correlations
Step 2, select good and bad examples
Step 3, apply outlier detection on selected examples. Grey sheep
users are the intersections of bad examples and identified
outliers
Step4, examine the quality of identified grey sheep users

12. Proposed Solution
12
Step 1, Distribution Representations
We calculate user-user correlations by cosine similarity
Obtain the descriptive statistics of the distribution

13. Proposed Solution
13
Step 2, Example Selection
Good examples: high correlations and left-skewed
Bad examples: low correlations and right-skewed

14. Proposed Solution
14
Step 3, Outlier Detection by Local Outlier Factor (LOF)
LOF helps identify outliers by the local density
Observations with LOF > 1 will be considered as outliers
We set different threshold values to find
the optimal one for identifying grey sheep
users, for example
LOF threshold = 1.0
LOF threshold = 1.1
LOF threshold = 1.2
LOF threshold = ….

15. Proposed Solution
15
Step 4, Examine the quality of identified GS Users
The parameters in our solution
Example Selection
LOF threshold
Neighbor of neighborhood in LOF method
Our goals or examination criteria
To find as many GS users as possible
Recommendation by UBCF should be worse for GS users than non-
GS users

16. Improved Approach
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Drawback
Cosine similarities reply on co-ratings. If two users did not rate
items in common, we are not able to measure their similarities
Improved Approach
We represent each user as its similarity distribution
The distribution can be represented by a histogram
The interaction of two histograms tells the user-user similarity

17. Experimental Setting
• Data: MovieLens 100K rating data
– 100K ratings
– 1K users
– 1.7K movies
– Each user has rated at least 20 movies
• Evaluation
– 80% as training, 20% as testing
– Mean absolute error, MAE, to eval rating predictions
17

18. Results
• Comparison of Different Approaches
18

19. Results
• Comparison of Recommendation Quality
19

20. Results
• Visualization of GS and Non-GS users
20

21. Conclusions
• We develop a novel approach to identify GS
users by utilizing the definition related to the
user-user correlations
• We propose to use histogram intersection to
better measure user-user similarities
• Our approach is demonstrated to work better
than others based on the MovieLens 100K data
21

22. Future Work
• Try it on other data sets
• Seek approaches to improve the
recommendation performance for the group of
Grey Sheep Users
22

23. Yong Zheng, Mayur Agnani, Mili Singh
Illinois Institute of Technology
Chicago, IL, 60616, USA
Identification of Grey Sheep Users By Histogram
Intersection In Recommender Systems