1. Context-driven Access to Personalized Digital Multimedia Libraries Invited Talk at the 1st International Conference on Digital Libraries New Dehli, India 24-27 February, 2004 Erich J. Neuhold Fraunhofer IPSI Darmstadt, Germany http://ipsi.fhg.de/~neuhold neuhold@ipsi.fhg.de

2. © Fraunhofer IPSI ICDL 2004, New Dehli 2 Content  Context of Information Access  Personalization in Digital Libraries  Classification of Personalization Methods  Recommender Systems  Next Generation Personalization  Personal Web Context  Personal Reference Library  Cooperative Annotation  Overcoming Road Blocks to Personalization

3. © Fraunhofer IPSI ICDL 2004, New Dehli 3 Context Definition Context is:  The sum total of meanings (associations, ideas, assumptions, preconceptions, etc.) that:  (a) are intimately related to a thing,  (b) provide the origins for, and  (c) influence our attitudes, perspectives, judgments, and knowledge of that thing.

4. © Fraunhofer IPSI ICDL 2004, New Dehli 4 Context of Information Access Tasks Skills Interests context of the user context of information object Links Annotations Metadata Relationships Environment context-driven information access

5. © Fraunhofer IPSI ICDL 2004, New Dehli 5 Ways of using Context  annotation create context for information objects and can be used to support cooperation and improved retrieval  putting relevant information object into a working context (structuring metadata)  personalization based on modeling the context of the user

6. © Fraunhofer IPSI ICDL 2004, New Dehli 6 Motivation: Personalization “Digital libraries that are not personalized for individuals will be seen as defaulting on their obligation to offer the best service possible” - Personalisation and Recommender Systems in Digital Libraries Joint NSF-EU DELOS Working Group Report, May 2003

7. © Fraunhofer IPSI ICDL 2004, New Dehli 7 DL: Content-to-Community Mediation Understanding of users in domain Providing enrichment Facilitating access

8. © Fraunhofer IPSI ICDL 2004, New Dehli 8 Challenges – (1): Targeting Content Content:  is becoming more voluminous  is becoming more varied  Contributes to information overload Community:  Is made of diverse individuals  Conflict:  Individual-specific information need  Holistically targeting entire community  Personalization as a solution?

9. © Fraunhofer IPSI ICDL 2004, New Dehli 9 Challenges – (2): Understanding Users Users have a Context:  Differing cognitive patterns (i.e. skills, interests)  Embedded in a Community  Multiple tasks or goals User have competing simultaneous roles that are:  Interactive  Related to other entities in a given domain  Autonomous  Require individual conceptualization of the information space  Personalization as a solution? Interaction Autonomy DL user Context

10. © Fraunhofer IPSI ICDL 2004, New Dehli 10 Personalization as a Solution Personalization dynamically adapts a system’s service or content offer, based on a model of the user, in order to better meet or support the preferences and goals of individuals and specific target groups [Riecken 2000] Objective of Personalization:  Goal oriented information supply  Reduction of individual information overload  Content-to-Individual Mediation

11. © Fraunhofer IPSI ICDL 2004, New Dehli 11 Checkpoints: Meeting the Challenges  User Models - complex, i.e. context-based:  Differing cognitive patterns (i.e. interests)  Relations to other domain entities  Individual conceptualization  Multiple tasks or purposes  Group Interaction:  Infrastructure supporting group interaction & information needs  Personalization - Group models

12. © Fraunhofer IPSI ICDL 2004, New Dehli 12 Personalization Methods and Cognitive Patterns There are several ways personalization can support user’s cognitive patterns: Personalization DL ServicesContent Special Service Properties Enrichment SelectionStructuring Notification Personal Agents Configuration Visualization Recommendation Annotation Rating Information Filtering Container Bookmarks Navigation Guided Tours Entry Point

13. © Fraunhofer IPSI ICDL 2004, New Dehli 13 Selection: Information Filtering TV Scout: TV program recommendation system  Information Filtering:  Selectivity from dynamic information sources on behalf of a user  Dynamic Information Filtering:  Information Filtering in the presence of rapidly changing user interests  user informs the system of their new interests  i.e. TV Scout [Baudish 2001]

14. © Fraunhofer IPSI ICDL 2004, New Dehli 14 Special Services: Notification ChangeDetect supports:  Email notification:  sends an automatic email whenever pages are updated  saves your favorite web pages  monitors content  FREE service  http://www.changedetect.com CheckDetect Control Panel

15. © Fraunhofer IPSI ICDL 2004, New Dehli 15 Enrichment: Recommendation There are several types of Recommender Systems:  Collaborative  Content-Based  Demographic based  Utility-Based  Knowledge Based  Hybrid

16. © Fraunhofer IPSI ICDL 2004, New Dehli 16 Types of Recommender Systems - Collaborative Collaborative = user-to-user  Based on similar users’ ratings  Rate movies you have seen  Receive online movie recommendations (red stars) Receiving a MovieLens Recommendation

17. © Fraunhofer IPSI ICDL 2004, New Dehli 17 Types of Recommender Systems – Content based Content-Based = item-to-item  correlation between the item’s content and user preference  Adaptive interface  Online Recommendations Receiving book recommendations at Amazon.com

18. © Fraunhofer IPSI ICDL 2004, New Dehli 18 Types of Recommender Systems – Demographic and Utility based Demographic-based:  First: categorize the user based on personal attributes  Second: filter based on similar demographic categories [Burke 2002] Utility-based:  Computation made on the utility of each item for the user [Burke 2002] Filtered Items Utility-based Filter Resource Features Demographic-based Filter Category_01 match f (user) 1 2 3 4 5 Grouped Resources Information Seeker 01 02 f(user) = utility function for a specific user

19. © Fraunhofer IPSI ICDL 2004, New Dehli 19 Types of Recommender Systems – Knowledge based Knowledge-based:  uses functional knowledge about how a particular item meets a user need [Burke 2002]  i.e.  Type of cuisine  Price range  Style of food Entrée Knowledge-based System Interface

20. © Fraunhofer IPSI ICDL 2004, New Dehli 20 Types of Recommender Systems - Hybrid Hybrid Approach:  Combination of filtering methods - current trend  Overcomes weakness single methods  Improves system performance Example:  Graph-based Recommender System for DL [Huang 2002]  Content-based and  Demographic-based Content-Based Filtering: Correlation between similar books Demographic-Based Filtering: Correlation between similar users (Huang 2002)

21. © Fraunhofer IPSI ICDL 2004, New Dehli 21 Checkpoint: Meeting the Challenges  User Models - complex, i.e. context-based considering:  Differing cognitive patterns (i.e. interests)  Relations to other domain entities  Individual conceptualization  Multiple tasks or purposes  Group Interaction:  Infrastructure supporting group interaction & information needs  Personalization - Group models

22. © Fraunhofer IPSI ICDL 2004, New Dehli 22 The Personal Web Context It is not uncommon for people within a community to discover resources (i.e. other persons, documents) via serendipitous means because they are (directly or indirectly) tied into some larger web of social connections by community involvement. Personal Web Context as a Model of the User:  Role of Communities examined  Relationships in the scientific domain identified  Relations exploited for personalization