1. Pat Langley Institute for the Study of Learning and Expertise Palo Alto, California and Center for the Study of Language and Information Stanford University, Stanford, California http://cll.stanford.edu/~langleylangley@csli.stanford.edu Adaptive User Interfaces for Personalized Services Thanks to D. Billsus, M. Chen, C.-N. Fiechter, M. Gervasio, M. Goker, W. Iba, S. Rogers, C. Thompson, and J. Yoo.

2. We now have more information and choices available than ever before, and we need help to handle them effectively. The Need for Personalized Assistance This has led to recommendation systems, which help users locate and select relevant items. But often we want personalized assistance that takes into account our individual preferences. However, such personalized response requires a user model or profile that is constructed in some manner.

3. Approaches to User Modeling Hand-craftedProfiles Adaptive User InterfacesData-MiningMethods Hand-craftedStereotypes IndividualProfilesStereotypicalProfiles ManualConstruction AutomatedConstruction

4. Definition of an Adaptive User Interface that reduces user effort by acquiring a user model based on past user interaction a software artifact

5. Definition of a Machine Learning System that improves task performance by acquiring knowledge based on partial task experience a software artifact

6. Applications of Adaptive User Interfaces Web browsing TV selection bookselection in-carnavigation apartmentselection Email filing news filtering interactivescheduling stocktracking

7. Inferring Individual User Profiles Our work focuses on content-based approaches to adaptive user interfaces, rather than on collaborative approaches. Tasks that require a user decision a user decision A description for each task Traces of the users decisions Mapping from task features onto user decisions Find

8. Navigation aides already exist in both vehicles and on the World Wide Web. One decision-making task that confronts drivers can be stated as: However, they do not give personalized navigation advice to individual drivers. Given: The drivers current location C; Given: The drivers current location C; Given: The destination D that the driver desires; Given: The destination D that the driver desires; Given: Knowledge about available roads (e.g., a digital map); Given: Knowledge about available roads (e.g., a digital map); Find: One or more desirable routes from C to D. Find: One or more desirable routes from C to D. The Task of Route Selection

9. The Adaptive Route Advisor

10. The Adaptive Route Advisor represents the driver model as a weighted linear combination of route features. Training cases: [x0,..., xn] is better than [y0,..., yn]. Time Distance Intersections Turns Cost w0 w1 w2 w3 Generating Training Cases The system uses each training pair as constraints on the weights found during the modeling process.

11. Personalized user models produce better results than generalized models, even when the latter are based on more data. Experimental Results on Route Advice

12. Many online news services are available on the World Wide Web, but few offer personalized selection. Another service that would benefit drivers can be stated as: Moreover, they are ill suited for use in the driving environment, where visual attention is a limited resource. Given: Topics and events that interest the driver; Given: Topics and events that interest the driver; Given: Recent news stories available on the Web; Given: Recent news stories available on the Web; Given: Knowledge about stories the driver has heard; Given: Knowledge about stories the driver has heard; Find: Stories to read the driver during the current trip. Find: Stories to read the driver during the current trip. The Task of News Reading

13. News Dude (Billsus & Pazzani, 1999)

14. Adaptive News Readers in the Marketplace

15. Many recommendation systems are available on the World Wide Web, including ones that suggest restaurants. A third type of service that would assist drivers can be stated as: However, they are not designed for use by drivers, who must keep their eyes on the road. Given: Some goal the driver wants to achieve (e.g., eat lunch); Given: Some goal the driver wants to achieve (e.g., eat lunch); Given: Constraints on destinations that achieve this goal; Given: Constraints on destinations that achieve this goal; Given: Knowledge about available nearby destinations; Given: Knowledge about available nearby destinations; Find: One or more destinations that satisfy the constraints. Find: One or more destinations that satisfy the constraints. The Task of Destination Selection

16. The Adaptive Place Advisor

17. Inquirer: Where do you think I should eat tonight? Advisor: What type of food would you like? Inquirer: What types are there? Advisor: You can say things like Chinese, Indian, and Mediterranean. Inquirer: Oh, maybe a French place with free parking. Advisor: What quality rating would you like? Inquirer: I don't care, as long as it's cheap. Advisor: Im sorry, I dont know any restaurants like that. Would you like to search for any price? Would you like to search for any price? Inquirer: No, I think I'd like Chinese instead. Advisor: In what city would you like to eat? Inquirer: How about Berkeley? Advisor: How does the Long Life Vegi House on 2129 University Avenue sound? Inquirer: Sure, that sounds fine. Adaptive Place Advisor (Before Adaptation)

18. Inquirer: Where do you think I should eat tonight? Advisor: What type of food would you like? Inquirer: Cheap Chinese. Advisor: What city do you prefer? Inquirer: How about Palo Alto? Advisor: How does Jing Jing Szechuan Gourmet on 443 Emerson sound? on 443 Emerson sound? Inquirer: Sure, that sounds fine. Adaptive Place Advisor (After Adaptation)

19. Speech Acts Per Conversation with Adaptive Place Advisor

20. INCA: An Adaptive Scheduler

21. BackFlip: Personalized Bookmarking

22. Personalized Music Delivery

23. A Personalized Travel Agent

24. An Adaptive Apartment Finder

25. An Adaptive Stock Tracker

26. Alternative Presentation Styles Sequential Classification Tweaked Set Ranked List

27. suggest initialize short-term profile initialize/ retrieve profile specify query modify present respond decide Decision Long-term profile Item database User query SuggestionUser Response Update profile Short-term profile A Flexible Framework for Adaptive Interfaces

28. Challenges in Developing an Adaptive Interface Formulating the Problem Engineering the Representation Collecting User Traces Utilizing Model Effectively Gaining User Acceptance Modeling Process

29. Contributions of the Research Our research program on adaptive user interfaces has produced: Although some issues remain, we understand adaptive interfaces well enough to apply them in practical services. a variety of artifacts that learn user preferences unobtrusively;a variety of artifacts that learn user preferences unobtrusively; evidence that this approach to user modeling is a general one;evidence that this approach to user modeling is a general one; experimental support for the effectiveness of these systems;experimental support for the effectiveness of these systems; an analysis of presentation styles possible for such systems;an analysis of presentation styles possible for such systems; a flexible framework for constructing them efficiently; anda flexible framework for constructing them efficiently; and clarification of issues that arise in their effective design.clarification of issues that arise in their effective design.

30. Directions for Future Research Despite clear progress on adaptive user interfaces, we must still: Together, these advances will lead us toward a society in which personalized computational aides are a regular part of our lives. design methods to combine stereotypes and individual profiles;design methods to combine stereotypes and individual profiles; create approaches that transfer user profiles across domains;create approaches that transfer user profiles across domains; apply these techniques to an ever wider range of problems;apply these techniques to an ever wider range of problems; utilize new sensors to collect data even less obtrusively; andutilize new sensors to collect data even less obtrusively; and develop complete physical environments that adapt to users.develop complete physical environments that adapt to users.

32. Dialogue Operators for Adaptive Place Advisor System Operators Ask-ConstrainAsks a question to obtain a value for an attribute Ask-RelaxAsks a question to remove a value of an attribute Suggest-ValuesSuggests a small set of possible values for an attribute Suggest-AttributesSuggests a small set of unconstrained attributes Recommend-ItemRecommends an item that satisfies the current constraints ClarifyAsks a clarifying question if uncertain about latest user operator User Operators Provide-ConstrainProvides a value for an attribute Reject-ConstrainRejects the proposed attribute Accept-RelaxAccepts the removal of an attribute value Reject-RelaxRejects the removal of an attribute value Accept-ItemAccepts the proposed item Reject-ItemRejects the proposed item Query-AttributesAsks system for information about possible attributes Query-ValuesAsks system for information about possible attribute values Start-OverAsks the system to re-initialize the search QuitAsks the system to abort the search