1. User Modeling, Adaptation, Personalization Part 2 ΕΠΛ 435: Αλληλεπίδραση Ανθρώπου Υπολογιστή

2. Schema of User-Adaptive Systems USER MODEL USER MODEL ACQUISITION USER MODEL APPLICATION INFORMATION ABOUT UADAPTING TO U 01/11/2013Τμήμα Πληροφορικής 2

3. Two steps  Content adaptation – what content is most appropriate for the current user based on the user model  Content presentation – how to most effectively present the selected content to the user 01/11/2013Τμήμα Πληροφορικής 3

4. Page-based approaches  Pre-defined pages  The adaptation mechanism selects the most appropriate page UM Select pages Show to user Advantages and disadvantages? 01/11/2013Τμήμα Πληροφορικής 4

5. Example: KBS Hyperbook Adaptive Information Resources Adaptive Navigational Structure Adaptive Trail Generation Adaptive Project Selection Adaptive Goal Selection http://wwwis.win.tue.nl/asum99/henze/henze.html 01/11/2013Τμήμα Πληροφορικής 5

6. Example: AHA Navigation frame (generated by the system) Content frame – combines fragments prepared by authors Inclusion/exclusion of links; Inclusion/exclusion of detail http://aha.win.tue.nl/ 01/11/2013Τμήμα Πληροφορικής 6

7. Dynamic approaches Content adaptation: –Dynamic selection of content –Dynamic structuring of the content Content presentation –Defining relevance and focus –Dynamic media adaptation 01/11/2013Τμήμα Πληροφορικής 7

8. Dynamic content adaptation Content automatically selected from: –Knowledge base, relevance measures (e.g. ILEX, STOP) –Bayesian networks expressing causal probabilistic relationships between variables from the domain (e.g. NAG) –User preferences model, importance measures (e.g. GEA, RIA) Content automatically structured: –Task- accomplished planners –Argumentation models –Conversation theories 01/11/2013Τμήμα Πληροφορικής 8

9. Example: ILEX http://www.hcrc.ed.ac.uk/ilex/ Domain Model The Content Potential Text Structure Syntactic Structure Presentational Forms Representation of Context 01/11/2013Τμήμα Πληροφορικής 9

10. Example: GEA (Carenini & Moore, 2001) User preferences in a hierarchical model (e.g. house, location, number of bedrooms) Argument structure tailored to user preferences (uses measure of relevance) Level of detail will differ for users or for the same user at different stages 01/11/2013Τμήμα Πληροφορικής 10

11. Example: RIA http://www.research.ibm.com/RIA/ Two different responses to the same query depending on user preferences 01/11/2013Τμήμα Πληροφορικής 11

12. Dynamic content presentation Maintaining focus and context Focus – emphasise the content that has been found most relevant to the user Context – allow access to less relevant content to preserve context –Stretch text –Scaling fragments –Dimming fragments –Summary thumbnail 01/11/2013Τμήμα Πληροφορικής 12

13. Follows the “fish eye” visualisation Technique Adaptation of an online guide about cultural events in Toronto: http://whatsuptoronto.com/ Example: scaling approach 01/11/2013Τμήμα Πληροφορικής 13

14. Dynamic content presentation Media adaptation: factors User-specific features Information-specific features Contextual information Media constraints Limitations of technical resources 01/11/2013Τμήμα Πληροφορικής 14

15. Dynamic content presentation Media adaptation: approaches Rule-based approaches –Using rules to define how to take into account the media factors in media selection Optimisation approaches –Given the media factors, find the media combination that produces the most optimal result 01/11/2013Τμήμα Πληροφορικής 15

16. Example: RIA Optimisation adaptation http://www.research.ibm.com/RIA/ The optimization procedure deals with: (1) suitability of the information to the media; (2) increase recallability; (3) maintain presentation consistency 01/11/2013Τμήμα Πληροφορικής 16

17. Recommender systems: intro  The problem:  too much content!  too many choices!

18. Recommendation Features

19. How do recommender systems work? Major types of algorithms  Collaborative or social filtering  Suggestion lists, “top-n” offers and promotions  Content-based recommenders  E-mail filters, clipping services  Hybrid recommenders  Suggestion lists, “top-n” offers and promotions

20. Collaborative filtering  Other’s ratings  What others like  My ratings  What I think like Give me what people similar to me would like “Word of mouth” “Voting”

21. Content-based Filtering  Content  Appropriate information about it  User profile  Relevant to the content Give me only those I like User Profile Content

22. Hybrid Filtering  Combining both  Building on advantages  Overcoming limitations User Profile Content

23. 01/11/2013Τμήμα Πληροφορικής 23 Recommendations/Similarities Similar friends (left) and recommended pages (right) based on user similarity

24. 18/10/2013Τμήμα Πληροφορικής 24 What do you think Amazon is using?

25. 01/11/2013Τμήμα Πληροφορικής 25 Καλή Συνέχεια