1. Personalization of the Digital Library Experience: Progress and Prospects Nicholas J. Belkin Rutgers University, USA belkin@rutgers.edu belkin@rutgers.edu

2. Focus of this Talk Individuals’ interactions with information and with information systems How to make such interactions effective and pleasurable Primarily concerned today with information seeking and searching interactions

3. Personalization Tailoring the interaction with information to the person’s –Goals, tasks –Context –Situation –Individual characteristics

4. Goals, Tasks What leads the person to engage in interaction with information –(Life) goal –Task to accomplish –Problematic situation Goals and tasks associated with information seeking and searching

5. Context Physical environment, e.g. –Spatial –Temporal Personal environment, e.g. –Current and previous behaviors –Affect Social environment, e.g. –Communication –Collaboration –Community

6. Individual Characteristics Knowledge, e.g. –Of task –Of topic Cognitive abilities, e.g. –Verbal –Spatial –Memory Preferences, e.g. –Learning style –Interaction style

7. Situation The particulars of goals, tasks, context, individual characteristics, at any one time following Cool,C.( 2001) The concept of situation in information science. In M. Williams (Ed.), Annual Review of Information Science and Technology, vol. 35, pp. 5-42.

8. A Familiar Reference Taylor, R.S. (1968) Question negotiation and information seeking in libraries. College & Research Libraries, 28, 178-194. Taylor’s five filters are an example of personalization, as accomplished by peoplefilters This represents the ideal of “intelligent” support for information interaction

9. Workshop on Intelligent Access to Digital Libraries First (?) scholarly meeting with “Digital Libraries” in title Indication of early recognition in the DL community of the users of DLs But, most early research, and practice, in DLs concerned with technical issues

10. “Access”/Total Titles ACM DL 19965/42 ACM DL 19978/46 ACM DL 19988/49 ACM DL 199910/58 ACM DL 200011/46 JCDL 200115/117

11. Initial Steps toward Personalization “Novice” and “Expert” interaction –Followed 2nd generation OPAC ideas –Effectively, “easy” and “advanced” search Self-tailoring of results –Beyond simple linear ordering – e. g., ENVISION project Wang, J. et al. (2002) Enhancing the ENVISION interface for digital libraries. In JCDL 2002 (pp. 275-276). New York: ACM.

12. ENVISION Interface

13. Self-Tailoring of Object Characteristics “Faceted” search Support for rapid integration of search modification and results, e.g. mSpace Wilson, M.L. & schraefel, mc (2008) A longitudinal study of keyword and exploratory search. In JCDL 2008 (pp. 52- 55). New York: ACM. Stuff I’ve Seen Dumais, S. et al. (2003) Stuff I’ve Seen: A system for personal information retrieval and reuse. In SIGIR 2003 (pp. 72-79) New York: ACM.

16. Automatic Personalization Based on evidence about the person’s: –goals, task; –context –situation –individual characteristics Evidence obtained: –Explicitly –Implicitly

17. Explicit Evidence for Personalization Explicit relevance feedback –The information system “learns” about the person’s information problem and reacts accordingly (Rocchio, 1971) Assumes an “ideal” query, representing a static information problem People seem unwilling to provide this explicit evidence

18. Implicit Evidence for Personalization Inferring rather than eliciting Evidence lies in the person’s –Current behaviors –Previous behaviors Evidence lies in behaviors of people “like” the person –The most common type of personalization in operational systems

19. Operational Examples of Personalization Search engine query completion –Based on your previous behavior –Based on others’ previous behaviors Recommendation of things you might like –Netflix: What you liked before –Amazon: What others who liked “this” also liked

20. “Reading” Behavior: 1 Based on “dwell time” –The longer one looks at a page, the more likely it is to be relevant/useful Morita, M & Shinoda, Y. (1994) Information filtering based on user behavior analysis and best match text retrieval. In SIGIR ‘94 (pp. 272-281). New York: ACM. –But, to be accurately interpreted, must take into account other aspects Kelly, D. & Belkin, N.J. (2004) Display time as implicit feedback: understanding task effects. In SIGIR 2004 (pp. 377-384). New York: ACM.

21. Reading Behavior: 2 Based on “click-through” –If a person “clicks” on a link, the object linked to is likely to be of interest Claypool, M., et al. (2001) Implicit interest indicators. In IUI 2001 (pp. 33-40). New York: ACM. But “click-through” can mean many things, and is an unreliable indicator on its own Joachims, T., et al. (2005) Accurately interpreting click-through data as implicit feedback. In SIGIR 2005 (pp. 154-161). New York: ACM.

22. Previous Use Behavior What a person has used or produced before can indicate what will be useful in the future Use what’s on the desktop for implicit relevance feedback Teevan, J., Dumais, S.T. & Horvitz, E. (2005) Personalizing search via automated analysis of interests and activities. In SIGIR ‘05 (pp. 449-456). New York: ACM.

23. Non-Relevance Feedback Personalization What a person already knows about a topic will affect what will be useful Kelly, D. & Cool, C. (2002) The effects of topic familiarity on information search behavior. In JCDL ‘02 (pp. 74-75). New York: ACM. The task that a person is engaged in will affect what will be useful Li, Y. (2009) Exploring the relationships between work task and search task in information search. JASIST, v. 60: 275-291.

24. “Helpful” Personalization Prediction of when a person needs help in information interaction, and what type of help would be useful Xie, H. & Cool, C. (2009) Understanding help seeking within the context of searching digital libraries. JASIST, v. 60: 477-494.

25. Multi-Faceted Personalization Many factors affect what information a person would like to interact with These factors interact with one-another White, R. W. & Kelly, D. (2006) A study on the effects of personalization and task information on implicit feedback performance. In CIKM ‘06 (pp. 297-306). New York: ACM. Accurate personalization will require multiple sources of implicit evidence of a variety of facets of personalization

26. The PoODLE Project Investigates behavioral evidence of: –knowledge of topic –type of task –stage of task completion –cognitive abilities Integrates this evidence with dwell time, previous use, to predict usefulness of information objects

27. Problems in Personalization for Digital Libraries Most personalization research is taking place in the information retrieval community There is a conflict between server-side and client-side personalization Some aspects of personalization remain to be considered (e.g. affect)

28. Prospects for Personalization in Digital Libraries Lots of interest in personalization of information interaction in general More research needed within the explicit domain of digital libraries Good prospects for integration of evidence from both server and client

29. Acknowledgements Much help from the PoODLE team http://scils.rutgers.edu/imls/poodle http://scils.rutgers.edu/imls/poodle Funding for this research comes from the U.S. Institute of Museum and Library Services