Personalized Information Services

1. Personalized Information Services Javed Mostafa Indiana University, Bloomington April 11, 2003

2. Outline • Personalization as part of a broader field • Personalization vs. customization • Representation • A research issue in personalization • Approaches taken to study the issue • Results • Conclusion Acknowledgment: The research described in this presentation is a collaboration among a number of people. I am grateful for work conducted by Dr. Mukhopadhyay & Dr. Palakal (Computer & Information Science, IUPUI). I am also indebted to two of my previous students: Luz Quiroga and Junliang Zhang. Thanks also to NSF for funding this research.

3. Connection to a broader field • Personalization is part of a larger field known as context aware computing (CAC) • CAC is concerned with a broad range of problems including development of smart environments (offices, homes, cars, etc.), smart weapons and appliances, smart clothing, and information systems • Some interesting projects: • Project Oxygen (MIT): http://oxygen.lcs.mit.edu/Overview.html • SmartSpaces (NIST): http://www.nist.gov/smartspace/smartSpaces/ • Adaptive Systems: Attentive User Interfaces • (Microsoft): http://www.research.microsoft.com/adapt/

4. Context Aware Information Services (CAIS) • Goal: Basic information “support” services (i.e., browse, search, filter, presentation and visualization) should be: seamlesslyavailable from any location, any device, or any application, and in a form that permits optimum use of the information

5. Context Aware Information Services (CAIS) • Context is complex • Users can interact with: • a variety of info systems: their desktop, a laptop, a handheld, or a palmtop • A variety of applications and documents • Users may be stationary or mobile

6. Levels in CAIS Tablet Desktop PDA MS-Word Photoshop MS-Excel Netscape Different types of documents and content Users interaction, users short term demands, user’ s long term needs

7. Requirements: Proactive awareness and responses • Proactively seek information related to content being manipulated by the user and bring related and relevant information to the user’s attention • Automatically modulate the features and presentation according to device and application characteristics

8. Contexts of a Typical User Location Device Applications Tasks Information Immediate and long-term info demands

9. Customization vs. Personalization • Customization = taking into account contexts other than those that represent personal information demands and interests (short- or long- term) • Personalization = taking into account contextual information related to user’s information demands and interests (e.g., query terms, relevance feedback on documents, rating, etc.) • Both, together, support context aware information services

10. Customization vs. Personalization Location Representation for customization Device Applications Tasks Representation for personalization Information Immediate and long-term info demands

11. Representation • To provide context aware info services requires maintaining up-to-date contextual information in a form that permits efficient computation and accurate predictions about user’s info needs, i.e., need context representation

12. Representation for Personalization: User Profile • We developed a representation to predict relevance of new information according to user’s interest and long-term information need • Requirements supported: • Online learning • Low latency • Permits exploration and adaptation

13. Generating the representation • To generate the representation we relied on rating or indicators of interest on topical categories • The representation contained two types of information: topical categories and assessment of interest in the categories

14. c1 c2 c3 : : cn Categories Interest representation for personalization Probability that category 2 is the most relevant category Probability that category 1 is relevant to the user t1 u1 t2 u2 t3 u3 : : : : Documents tn un Top class Relevance of categories User profile/model

15. Source of interest information • Explicit: User’s were asked to provide rating on documents • Implicit: User’s interaction with content and the interface were taken into consideration • Such interest information was converted into the (two-level) profile/model by using a simple RL algorithm: • Mostafa et al. A multilevel approach to intelligent information filtering: Model, system, and evaluation. ACM TOIS, 15(4), 1997. • Different applications have been created, incl. SIMSIFTER and TuneSIFTER • See: lair.indiana.edu/research/

16. Research issue: Big picture • Interested in two types of research issues: • With any type of intelligent HCI a fundamental issue is control • Who is in charge? • If the user wishes to delegate, how much autonomy should the system have? • Agent vs. User (Direct Manipulation) • Maes & Shneiderman debate: http://www.acm.org/sigchi/chi97/proceedings/panel/jrm.htm • If the user wishes to take charge, how much responsibility should the user take on? : User effort … user involvement can impact system effectiveness

17. A research issue: User’s Role in Personalization • Type of interest • Interest change • User Involvement • Amount of interaction • Type of interaction

18. Approaches to study the research issue • As it is v. difficult to manipulate certain conditions (e.g., change of interest w.r.t. certain topics) we developed a simulation tool • For other conditions we conducted experimental studies with actual users

19. Simulation study using SIMSIFTER • Type of interest may impact the rating (degree and frequency) • Rating may impact how quickly the system can “learn” or generate an accurate profile • Accuracy of profile determines accuracy of prediction of relevance • SIMSIFTER used about 1.4K consumer health documents and 15 categories of health information (anxiety, allergy, heart, cholesterol, depression, diet, environment, exercise, eye, headache, lung, medicine, teeth, men-health, and women-health )

20. Study: Different Profile Types • We created different types of profiles – concrete, middle, and mild-low • Degree of interest was used to generate rating probabilistically • Frequency of rating increases with increased intensity of interest

21. Results: Different Profile Types Impact of different types of interest on prediction of relevance

22. Study: Change in Interest • Over time as the user is exposed to continuous flow of new information and user’s situation changes, the user may experience change in interest • Change in interest may be gradual or abrupt

23. Results: Change in Interest Impact of change in interest on prediction of relevance

24. Study: Modalities of interest information collection • Interest information can be collected explicitly by asking the user • By generating the rating based on content viewed by the user • Or, a combination of both of the above strategies

25. Results: Different modalities of interest information collection Impact of different interest information collection modalities on prediction of relevance

26. TuneSIFTER Study • Aim was to engage actual users and analyze different modalities of interest information collection • Rule-based • Explicitly by requiring users to rate • Implicitly by observing behavior and associating behavior with rating • Provided access to music titles in a dozen genre from the MP3.com service • 35 subjects recruited from IUB

27. TuneSIFTER User Interface

28. Study:Three modalities of interest information collection • Rule based = user provided the profile in the first session • Explicit learning = user rated music titles • Implicit learning = different sources used: user’s click on the column of title, user’s click on the column of artist name, user’s click on the column of genre, and user’s click on the column to request more information. In addition, the time user spent on listening to the music was also recorded by the implicit-learning system

29. Results: Three modalities of interest information collection

30. Conclusions The representation and the learning approach developed are quite robust in terms of capturing different types of interest and change in interest Implicit modality, when time data is available, may be applicable in reducing user involvement without sacrificing performance

31. Limitations and Future Work • User involvement may vary with tasks and domains • For example Kelly and Belkin (2002) state that reading time is not a reliable source for implicit modeling • Different levels of modeling may be needed • Topical granularity in the user profile influences performance – Quiroga and Mostafa (2002) • Two-level modeling needed in the News domain (content highly dynamic)

32. Additional Citations • Kelly and Belkin. Modeling characteristics of the User’s Problematic Situation with Information Search and Use Behaviors. JCDL Workshop on Document Search Interface Design, http://xtasy.slis.indiana.edu/jcdlui/uiws.html, 2002. • Quiroga and Mostafa. An Experiment in Building Profiles in Information Filtering: The Role of Context of User Relevance Feedback. Information Processing & Management, 38(5), 2002. • Pitkow et al. Personalized Search. CACM, 45(9), 2002. • User modeling 10th Anniversary Issue. Gerhard Fischer’s work in this area is especially recommended.

33. Related IR Forums • SIGIR - ACM Special Interest Group on Information Retrieval Conference • UIST  - ACM User Interface Software & Technology Conference • UIU - ACM Intelligent User Interfaces Conference • TREC - Text REtrieval Conference • ASIST - American Society for Information Science and Technology Conference • JCDL - Joint Conference on Digital Libraries • CIKM - Conference on Information and Knowledge Management • AGENTS - International Conference on Autonomous Agents

34. Need more information? • Our lab: • Laboratory of Applied Informatics Research (lair.indiana.edu) • Email: jm@indiana.edu