# Digital Libraries/Personalization
## Module name

Personalization

## Scope

This module addresses Personalization standards that may be appropriate in the context of a DL, along with its’ approaches, effects, limitations, and challenges.

## Learning objectives:

By the end of this module, the student will be able to:
       a. Have a clear understanding of what Personalization is and how it can affect Digital Libraries
       b. Understand various personalization approaches
       c. Understand the limitations and challenges of Personalization

## 5S characteristics of the module:

       a. Society: Where all other personalization dimensions would be organized or targeted for particular societies of users, e.g., incorporation and adaptation of specialized services for librarians, professors, and students in a digital library of theses and dissertations
       b. Scenarios: Like scenario re-design, by introducing new functions and interaction techniques, e.g., navigation by context, or by specializing existing ones, e.g., changes in syntax and parameters for searching
       c. Spaces: Such as mappings between different spaces (e.g., from vector space models to probabilistic ones) for interoperability or reduction of dimensionality for providing better search services (e.g., with Latent Semantic Indexing (LSI))
       d. Structures: Including restructuring, reduction, or other transformations over classification systems, ontologies, internal structures of documents, etc
       e. Stream: Which could include, in the case of textual streams, translations of language and conversion of encodings, or, in the case of multimedia data, possible conversions between formats according to a user's platform

## Level of effort required:

       a. In class: 1.25 hours
       b. Outside of class: 2-3 hours for readings

## Relationships with other modules:

       a. 6-d: Interaction design, usability assessment
          i. Personalization could affect the usability of a digital library
       b. 9-c: DL evaluation, user studies
          i. Personalization can affect a user’s opinion of a DL and have an impact on the user studies

## Prerequisite knowledge required:

       a. Students will not be expected to have had prior training in personalization.

## Introductory remedial instruction

       a. none

## Body of Knowledge

    1. What is Personalization?
       a. Tailored Services
       b. Adapting Presentation
       c. Make DL’s accessible
       d. Device Examples
          i. Eye track
          ii. Sense Cam
          iii. Preservation migration
    2. Goals
    3. User Centered
       a. Personal Information
          i. Profile
          ii. Age
          iii. Sex
          iv. Health
          v. Activities
          vi. Commerce
          vii. Modeling Queries
       b. Personal View on Information World
          i. Distribution
          ii. Services
             1. Intelligent Tutoring Systems
          iii. Broadcast, on demand
          iv. Alerts (Notifications), Interruptions
          v. Trails, bread crumbs
       c. Capturing Personal Information
          i. Eye tracking
          ii. Click through logs
          iii. Privacy
          iv. Analysis
    4. Classification of Personalized Methods
       a. Content
          i. Structuring
          ii. Selection
          iii. Enrichment
       b. Services
          i. Special Services
          ii. Service Properties
    5. Information Filtering
    6. Notification
    7. Recommender System
       a. Definition
          i. Personalization Service
          ii. Most popular DL personalization
          iii. Tailors information to individuals
       b. Scope
       c. Components
          i. Background Data
          ii. Input Data
          iii. Algorithm
       d. Types
          i. Content-based
             1. Contextualization
          ii. Knowledge-based
          iii. Collaborative Information Filtering
          iv. Demographic-based
             1. In the community
             2. Multiple memberships
             3. Roles
          v. Utility-based
          vi. Hybrid
             1. integrated information inference
          vii. Community-based
    8. Advanced Approaches for Personalization
       a. Personal Reference Libraries
       b. Cooperative Content Annotation
       c. Personal Web Context
    9. Social Effects of Personalization
       a. Individual Experience
       b. Community Experience
       c. Social Groups
    10. Personalized Information Environment (PIE)
       a. Collection Personalization
          i. Personalized Filtering
          ii. Personalized Retrieving
       b. Material Personalization
    11. Evaluation Issues
       a. Access Personalization
       b. User-Centered Evaluations
       c. Identify Appropriate Criteria
       d. Training and Testing
       e. Usability
       f. Usefulness
       g. Performance
       h. Failures
          i. Types
          ii. Causes
          iii. Solutions
       i. Benefits
          i. Memory
          ii. Symbiosis
    12. Limitations and Challenges
       a. Privacy
       b. Hinder Findings
       c. Hinder Group Communication
       d. Predictability

## Resources
### Assigned readings for students

    i. Beaulieu, Micheline, Borlund, Pia, Brusilovsky, Peter, et al (2003). Personalization and Recommender Systems in Digital Libraries. Joint NSF-EU DELOS Working Group Report. Retrieved 11/6/2008 from: http://www.ercim.org/publication/ws-proceedings/Delos- NSF/Personalisation.pdf
    ii. Bollen, Johan, Di Giacomo, Mariella, Mahoney, Dan, Monroy-Hernandez, Monroy, Ruiz Meraz, Cesar M (2001). MyLibrary, A Personalization Service for Digital Library Environments. Joint DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries, Dublin, Ireland, Retrieved 11/9/2008 from: http://www.ercim.org/publication/wsproceedings/DelNoe02/Giacomo.pdf
    iii. Jewagamage, K. Priyantha, Hirakawa, Masahito, Jayawardana, Champu (2001). Personalization Tools for Active Learning in Digital Libraries. MC Journal: The Journal of Academic Media Librarianship. Retrieved 11/1/2008 from http://wings.buffalo.edu/publications/mcjrnl/v8n1/active.pdf
    iv. Neuhold, E.J., Nieder´ee, C., Stewart, A. (2003). Personalization in digital libraries: An extended view. Proceedings of ICADL 2003. (pp. 1–16) Retrieved 11/1/2008 from http://www.springerlink.com/content/wc7kybhvtpuaf1y3/fulltext.pdf
    v. Dumais, Susan T, Liebling, Daniel J, Teevan, Jaime. To Personalize or Not to Personalize: Modeling Queries with Variation in User Intent. Retrieved 11/4/2008 from http://portal.acm.org/citation.cfm?id=1390364
    vi. Aalbert, Trond, Agosti, Maristella, Fuhr, Norbert, et al. Evaluation of digital libraries (2007). Retrieved 11/4/2008 from http://www.springerlink.com/content/w2w0j4h272k26812/fulltext.pdf

### Recommended readings for students

    i. M. Elena Renda, and Umberto Straccia (2005). A personalized collaborative Digital Library environment: a model and an application. Inf. Process. Manage., Vol. 41, No. 1., pp. 5-21. Retrieved 11/2/2008 from http://www.sciencedirect.com/science/article/B6VC8-4CG0NPT-2/2/91d50b6b47b390024411fc8d594da812
    ii. Chia, Christopher, Garcia, June (2002). The personalized challenge in public libraries: perspectives and prospects. Retrieved 11/2/2008 from http://www.publiclibraries.net/html/x_media/pdf/personalisation_engl.pdf
    iii. Boardman, R. (2002). Workspaces that work: Towards unified personal information management. In Proceedings of HCI2002, People and Computers XVI—Memorable Yet Invisible. Volume 2, 216–7, London. Retrieved 11/10/2008 from http://www.iis.ee.ic.ac.uk/~rick/research/pubs/wthatw-hci2002.pdf
    iv. Jung, Jason J. Personalized Information Delivering Service in Blog-Like Digital Libraries. Retrieved 11/10/2008 from http://www.springerlink.com/content/5312380505287115/fulltext.pdf

### Recommended background reading for instructor

    i. Goncalves, M., Zafer, A. A., Ramakrishnan, N., & Fox, E. A. (2001). Modeling and building personalized digital libraries with PIPE and 5SL. In Proceedings of the 43rd Joint DELOS-NSF workshop on personalization and recommender systems in digital libraries, Dublin, Ireland (pp. 67–72)
    ii. Neuhold, Erich (2004) Context Driven Access Personalized Digital Multimedia Libraries. Paper presented at the ICDL 2004 Conference hosted by TERI, New Delhi, India. Retrieved 11/9/2008 from https://drtc.isibang.ac.in/handle/1849/177

## Concept map

    None

## Exercises / Learning activities

    1. Pick a Digital Library and determine five ways in which you would like to personalize it. Discuss it with the rest of the class.
    2. Look at a Digital Library and name three ways you think personalizing it would benefit the users. Discuss it with the rest of the class.

## Evaluation of learning outcomes

    a. none

## Glossary

    a. none

## Additional useful links

    None

## Contributors

    a. Initial authors:
       Ashley Robinson
       Chester Rosson
       Pramodh Pochu
       Sheng Guo
    b. Evaluator:
       Edward A. Fox