Context Driven Access Personalized Digital Multimedia Libraries

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

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

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,

Context of Information Access

Tasks

Skills

Interests

context of the user

context of

information object

Links Annotations Metadata

Relationships

Environment

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}

Motivation: Personalization

“Digital libraries that

are not personalized

for

individuals will be seen as defaulting on their

obligation to offer the best service possible”

DL: Content-to-Community

Mediation

Understanding of users in domain

Providing

enrichment Facilitating

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

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

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}

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 Methods and Cognitive

Patterns

There are several ways personalization can support user’s cognitive patterns:

Personalization DL

Services Content

Special

Service PropertiesService Enrichment

Selection Structuring

• Notification

• Personal Agents

• Configuration

• Visualization

• Recommendation

• Annotation • Information Filtering

• Container • Bookmarks • Navigation

Selection: Information Filtering

 _{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

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

Enrichment: Recommendation

There are several types of Recommender Systems:

_{Collaborative} _{Content-Based}

_{Demographic based} _{Utility-Based}

Types of Recommender Systems -

Collaborative

Collaborative = user-to-user

_{Based on similar users’}

ratings

_{Rate movies you have seen}

_{Receive online movie}

Types of Recommender Systems –

Content based

Content-Based = item-to-item

_{correlation between the item’s}

content and user preference

_{Adaptive interface}

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

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

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:

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

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}