Profiling Users' Preferences with Text Mining '14
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Profiling Users' Preferences with Text Mining '14
- 1. Profiling Users’ Preferences with Text Mining Pedro Costa ISCTE-IUL 2014 Lisboa, 3 de Julho
- 2. Agenda Introduction Background and Related Work Plan2See Method Plan2See Setup Conclusions and Future Work
- 3. Introduction
- 4. Introduction Context ✓ The Internet usage is doubling every year ✓ Web is a network with large amounts of resources ✓ Our prototype is built on top of resources usage
- 5. Introduction Motivation ✓ Discovery of patterns & trends ✓ Text Mining = Data Mining for unstructured text ✓ Can be of use for analyzing existing Web usage
- 6. Introduction Research Question “Is it possible to group textual resources to users’ profiles and thus improve clustering techniques used in recommendation applications, without additional tagging mechanisms?”
- 7. Introduction Assumptions 1) personal information will always be insufficient; 2) tagging resources relies in human knowledge and sense to be accurate.
- 8. Introduction Our goal Our goal is to find an alternative method to classify new items as relevant or not, given all historical choices and at the same time use similar users’ choices to identify potentially relevant items.
- 9. to find an alternative method to classify new items as relevant or not, given all historical choices and at the same time use similar users’ choices to identify potentially relevant items. Background and Related Work Text Mining <> Data Mining ✓ Built on top of unstructured data ✓ Requires additional computing for natural language processing
- 10. to find an alternative method to classify new items as relevant or not, given all historical choices and at the same time use similar users’ choices to identify potentially relevant items. Background and Related Work Clustering ✓ Finds groups of similar objects ✓ Does not requires training sets ✓ Objects classification may be made afterwards
- 11. to find an alternative method to classify new items as relevant or not, given all historical choices and at the same time use similar users’ choices to identify potentially relevant items. Background and Related Work Text Mining ✓ Documents are represented as points in a space map ✓ Words are categorized and represented by frequencies on a dictionary ✓ It’s possible to apply classification or association techniques on those frequencies, as if it was plain numerical data
- 12. to find an alternative method to classify new items as relevant or not, given all historical choices and at the same time use similar users’ choices to identify potentially relevant items. Background and Related Work Building profiles (1) ✓ Tagging resources enables communities to search related resources without additional computation ✓ Requires someone to describe resources for the tag to be effective
- 13. to find an alternative method to classify new items as relevant or not, given all historical choices and at the same time use similar users’ choices to identify potentially relevant items. Background and Related Work Building profiles (2) ✓ Experiments in building profiles include analyzis of zones of interest or page links ✓ They are mostly based in users’ actions taken individually
- 14. to find an alternative method to classify new items as relevant or not, given all historical choices and at the same time use similar users’ choices to identify potentially relevant items. Background and Related Work Building profiles (3) ✓ Recommendation based in classification techniques require training with initial profiles ✓ Some authors recognize the subjectivity of users’ based profiles
- 15. Plan2See Method Recommendation ✓ Presents similar textual resources based on users’ selections ✓ Is based in the organisation of clusters built on top of users’ choices, thus dividing or grouping resources Resources ✓ Event announcements with title, description, date and location
- 16. Plan2See Method
- 19. T-Test result shows related content Plan2See Method Grouping
- 23. T-Test result shows unrelated content Plan2See Method Dividing
- 26. Plan2See Method Testing Equal Means ✓ We’ve used Hotteling 2-Sample T-squared Test for testing if the null hypothesis should be rejected Assumptions ✓ Only the 5% higher frequencies’ words are used ✓ Dividing is done for clusters with less than 60% of selected events ✓ Grouping is done on clusters with at least 25% of its’ events selected ✓ Clustering is schedule so clusters are stable and are not being modified for
- 27. ✓ Resources were gathered by crawling ✓ Data has been filtered to build the application dictionary ✓ We’ve tested 10 initial clusters from KMeans and decided to use only one initial cluster ✓ We’ve tested the basic operations for the algorithm with success Plan2See Setup
- 28. Conclusions ✓ A new method is proposed ➢ Clustering for users’ profiles ➢ Does not need any additional tagging mechanisms ➢ Clusters seem to be stable even if changes occur periodically
- 29. Conclusions ✓ Lacks tests with real users’ preferences ➢ Lacks testing recommendation for users’ items and for the dynamic groups ➢ Lacks verification that this profiling is effective, i. e., users are choosing similar contents in groups or communities