1. Using Semantic Web Technology to Improve Recommender Systems Based on Slope One
Rui Yang, Wei Hu and Yuzhong Qu
Department of Computer Science and Technology, Nanjing University, China

2. recommender systems
A moive ?
Recommender systems

3. Weighted Slope One(WSO)
Customer
item1
item2
item3
John 5 3 2
Mark 4 ?
Lucy
1. The average difference in ratings between item 2 and 1 is (2+(-1))/2=0.5.
Hence, on average, item 1 is rated above item 2 by 0.5.
2. The average difference between item 3 and 1 is 3.
3. Hence, if we attempt to predict the rating of Lucy for item 1 using her rating for
item 2, we get = 2.5.
4. If we try to predict her rating for item 1 using her rating of item 3, we get 5+3=8.

4. Problems
1. The user’s personality can not be covered in some especial cases.
Example: Harry Potter phenomenon.
2. Traditional dataset: lack of useful attribute information about the items. (Especially at the beginning time)

5. Overview of the approach

6. 1. Dataset identification
DBpedia
Freebase
LinkedMDB
DBTropes …
A tvtropes.org wrapper, providing data about 1,700+ movies and 2,500+ tropes/features.
Now about 10,000,000 RDF statements, 22,000 items, 22,000 feature types, and 1,750,000 feature instances.

7. Overview of the approach

8. 2. Mapping RDF resources to items
What methods we used?
String matching
TIP: Every film entity is identified
by a dereferenceable URI like
It’s case by case
For a book recommender system ISBN
For a paper recommender system the paper
titles, authors, journals and institutes (A VSM?)

9. Overview of the approach

10. 3. LDSD computation
What is LDSD?

11. 3. LDSD computation
What is LDSD?

12. 3. LDSD computation
What is LDSD?

13. 3. LDSD computation Linked Data Semantic Distance What is LDSD?
(Passant, A. AAAI 2010)

14. Overview of the approach

15. 4. Improved Slope One scheme
Weighted Slope One(WSO)
Non-linear Transformation of WSO

16. 4. Improved Slope One scheme
Weighted Slope One(WSO)
Linear Transformation of WSO

17. Evaluation Offline experiment User study Online experiment
Nanjing University

18. Rating Prediction

19. Rating Prediction

20. Top-N Recommendation and Coverage

21. Future work
More general mapping methods especially at the instance level
New approaches for incremental semantic distance computation to support dataset update
Integrate not only Slope One CF algorithm but also some sophisticated CF algorithms with Linked Data and Semantic Web technologies

22. Thank you
Q&A