Summary
- To provide the analytics module for the skills recognition and profiling of users based on their activity and preferences
- To provide a model (i.e., ontology for modeling personal skills and competencies) and implementation for the User Profiling Module of SlideWiki
- To implement a supervised classification method for enrichment of user profiles from data collected by the Student Information System
- To reuse and extend open-source tools (Weak, Apache Mahout, Apache Spark MLib)
Background and goals
User Profiling is applied on two levels, static and dynamic. Thus, SlideWiki will follow a hybrid model for User Profiling.
Static data for user profiling include the user-related data that the user has provided through the creation and updating of his/her profile. These include:
- Demographic data
- Name
- Age
- Education
- Educational and Research Interests
- Affiliation
- Participation in Groups
Dynamic data for user profiling include extracted and inferred data from user activity. These include:
- Deck consumption statistics
- Average time spent on reading/consuming decks
- Number of presentations given
- Usage of external sources, multimedia etc.
- Themes and Domains of created decks (e.g. through semantic annotation and/or tagging)
- Content creation
- Average time spent on creating/editing content
- Number of decks the user is participating as an owner/editor
- Statistics on usage of the user's created decks from other users (i.e., reachability/popularity of created content)
- Themes and Domains of created decks (e.g. through semantic annotation and/or tagging)
Assumptions
The user profiling module should interact with the activity tracking components in order to ensure that the needed data is produced (e.g. tracking of time spent on decks)