1. Personalizing the Digital Library Experience Nicholas J. Belkin, Jacek Gwizdka, Xiangmin Zhang SCILS, Rutgers University nick@belkin.rutgers.edu http://scils.rutgers.edu/imls/poodle

2. Goals of Personalization To make the user’s interaction with information as effective and pleasurable as possible To tailor the user’s interaction with information to the user’s characteristics, preferences, the specific circumstances of the interaction, and the user’s goals

3. Types of Personalization With respect to predictions of usefulness/ relevance of items, e.g. –modify query –re-rank results With respect to interaction, e.g. –different interface designs for different tasks –different interface designs for different individuals

4. Facets of Personalization Viewing/saving/evaluating behaviors Task Problem state Personal characteristics Personal preferences Context/situation

5. Viewing, etc. Behaviors Implicit evidence (Kelly & Teevan) –Time on “page” –Click-through –Previous uses –Others like the interactant Explicit evidence –Relevance feedback (of various sorts)

6. Task “Everyday” or “leading” or “work” task –Complexity, difficulty, “type” (Bystrom, et al.) Information seeking task –Choice of strategies, sources (Bates, Pejtersen, berrypicking) Information searching task –Moves, shifts (Bates; Xie)

7. Problem State What has been done before –Previous searches Stage in the Problem Solving Process (Kuhlthau; Vakkari) What is being done now –Immediately past behavior in searching, other concurrent activities

8. Personal Characteristics Knowledge –of topic, of task Demography –gender, age Individual differences –Cognitive abilities –Affect

9. Personal Preferences For types of interaction –Mixed or single initiative For styles of interaction –Display, navigation For support for interaction –Active, passive –Integrated, separate For types of information –Genre, level

10. Context, Situation Location –Physical environment –Mobile, static Salience Urgency Time –of day, of week, of month, of season, … Other interactants –Group conditions Social norms

11. Overall Goals for Personalization Determining significant aspects of each facet Determining means for identifying these aspects Determining means for implementation of support Integrating all facets of personalization into single system frameworks

12. Evidence for Personalization Explicit evidence, e.g. –Relevance judgments –Statements of goals, problems, etc. –Location; time of day, week, month, year Implicit evidence, e.g. –Dwell time –Clickthrough –Past searches, uses –Concurrent activities

13. Interpreting Implicit Evidence Dwell time is evidence of usefulness / relevance / interestingness –But needs to be interpreted in terms of task (Kelly, 2004; White & Kelly, 2006) –Is dependent on individual characteristics (Kelly, 2004) In general, evidence from any one facet could affect interpretation of evidence from any other facet All evidence is probably individual-dependent

14. Our Approach Investigate in depth aspects of specific facets, e.g. –Task –Domain knowledge –Cognitive characteristics Investigate the interactions among the different facets Implement and test within an integrative system framework Using a client-side “personalization assistant”

15. Initial Investigation Three months of logs of all computer use and searching behavior for each of seven Ph.D. students Judgments, by subjects, of usefulness of pages viewed as results of searching, with task type, duration and stage of task, topic, and familiarity with topic Both from Kelly (2004).

16. Data Analysis Exploratory analysis of relationships among dwell time and each of: task and topic familiarity; task stage; and, task duration, to determine most accurate dwell time value for predicting usefulness Exploratory analysis of current and past behavior as indicator of task type, task stage, and task/topic familiarity

17. Results to Date for Task Stage

18. Results to Date for Task Type

19. Results to Date for Topic Familiarity The three-way interaction of individual*usefulness*topic familiarity was significant, meaning that considering both the individual and topic familiarity information may be helpful in predicting usefulness by display time.

20. Next Steps Begin three concurrent investigations of –Domain knowledge –Task –Cognitive characteristics Each investigation to consider one additional facet Each to identify: –Evidence for particular facet –Use of evidence for personalization –Interaction of main facet with one other

21. Then … Implement results from previous investigations in prototype system Experiments to test methods of identification of evidence, and the use of that evidence from all facets simultaneously

22. And Finally Move from experimental prototype to robust client-side personalization assistant Distribute assistant to subjects in a real work environment Compare performance, usability, acceptability, etc. between those with, and those without the personalization assistant Make the personalization assistant available as open source software