1. Data-Driven Educational Data Mining ---- the Progress of Project
Presenter: Jun Ge
Supervisors: Jimmy Huang,
Marina G Erechtchoukova
Partner: Shore Consulting Group Inc.
Joseph Siahou, Scott McPhee

2. Agenda
Introduction
Content based Semantic Enhanced Recommendation System
Basic Approaches
Methodology
Challenges
Query Expansion based on Domain Taxonomy
General process and key point
Trial result
Challenges and further steps
Reference
Acknowledgement

3. Introduction Basic info Data:
Curricula in several Canadian provinces
Course resources, e.g. textbook, PPT, and pdf file etc.,
Teachers’ demographic information
The main idea is to connect the government, teachers and students with a system which can help teachers to find resources that meet government curricula standards and also can help students to improve their performance.

4. Recommendation System----Basic Approaches
Content-based filtering
Recommend items similar to those a given user has liked in the past
Collaborative filtering
Rrecommendation based on the ratings or behavior of other users in the system
Hybrid approach

5. Recommendation System----Methodology
Using semantics and a taxonomy to enhance personalized recommending process
Key Component
Item Representation
User Profile
Strategy for matching the two above

6. Recommendation System
Advantages:
Broadening the recommendation set by integrating semantics in the personalization process
New items have higher possibilities to be recommended
Personalized
Problems and Challenges:
Lack of data -- user profile and user interacts
Unstructured data:
Raw pdf files, no attributes with well-defined values
Complexity of natural language
Not a state-of-the-art technology

7. Query Expansion Based on Domain Taxonomy
General information retrieval process
Use Google as search engine
Focus on new query construction

8. Query Expansion Key point: Context:
system records( e.g. history queries, viewed documents)
user background( e.g. domain specific info)
task-related context( e.g. subject domain)
Strategies: according to how the contextual info acquired
Relevance feedback: users make relevance judgments
Knowledge models: ontology( e.g. domain specific)

9. Query Expansion----Bloom’s Taxonomy
What is Bloom’s Taxonomy?
Created in 1956
Promote higher forms of thinking in education
used when designing educational, training, and learning processes

10. Query Expansion----Methodology
Implement result:
implemented

11. Query Expansion Advantages: Challenges: Task-related
Heuristic, easy to implement
Challenges:
How to assign weights to each term in the new query?
How to utilize the categories of Bloom Taxonomy?
How to evaluate the performance?

12. Reference
Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook (pp ). Springer US.
Eirinaki, M., Vazirgiannis, M., & Varlamis, I. (2003, August). SEWeP: using site semantics and a taxonomy to enhance the Web personalization process. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp ). ACM.
Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The adaptive web (pp ). Springer Berlin Heidelberg.
Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web (pp ). Springer Berlin Heidelberg.
Guo, X., Zhang, G., Chew, E., & Burdon, S. (2005, December). A hybrid recommendation approach for one-and-only items. In Australasian Joint Conference on Artificial Intelligence (pp ). Springer Berlin Heidelberg.
Bhogal, J., MacFarlane, A., & Smith, P. (2007). A review of ontology based query expansion. Information processing & management, 43(4),
Wu, J., Ilyas, I., & Weddell, G. (2011). A study of ontology-based query expansion. Technical report CS-2011–04.

13. Acknowledgement
The project is conducted in collaboration with the industrial partner - Shore Consulting Group Inc. We are thankful to Big Data Research and Analytics Information Network for continuing financial support. This work is also supported by an ORF-RE (Ontario Research Fund - Research Excellence) award in BRAIN Alliance.1
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