Dec 2003, DRTC© C.Watters 1 Users and the Digital Library Carolyn Watters Dalhousie University...

Dec 2003, DRTC © C.Watters1

Users and the Digital Library

Carolyn Watters

Dalhousie University

Halifax, Canada

Dec 2003, DRTC© C.Watters2

Are Digital Libraries Libraries?

Phase I -Electronic access to traditional library Phase II- Access to electronic documents Phase III- Access to all information that is digital

– Communities of interest– Personal– Archival– Current– Editions

Phase IV - Semantic Web


Ranganathan’s 5 Laws and the DL

Books are for use. Every book its reader. Every reader its book. Save the time of the reader. Library is a living organism.


This Talk: Users of Digital Libraries

Task: what kind of information? Motivation: why is the user interacting? Interventions: what can we change?

– Query– Matching– Ranking– Presentation

Conclusions: what is effective?


Task: what kind of information?

Research– I am doing a study on Mesopotamia

Search– Who was the Prime Minister of India in 1962

Refind– What was the name of that movie I read the review on?

Browse– I am interested in Post-modern Art


Impact of Motivation & Task

Type I - Uses & Gratification Task– Satisfaction is in the result– News reading ?

Type II - Ludic Task– Part of the satisfaction is in the process– News reading?


Type I Tasks – Uses and Gratification tasks

The uses and gratification theoretical perspective is based on the assumption that the reader has some underlying goal, outside the reading itself, that reading satisfies.

Having the answer is the goal May be intrinsically motivated (i.e. may just

want to know) Traditional information need


Types of U&G Tasks

Research Queries– History of Kerala– Breadth important– Multiple viewpoints/sources expected

Question and Answer– Capital of Kerala– Accuracy important– Contradiction not expected


Characteristics of U&G Info Tasks

Articulation of a specific query

Recognition of relevance of retrieved results

User has control of “satisficing” point

* Herbert Simon (1976)


Can we make predictions for U&G tasks?

A study* of reading the news suggested that a neural net model was able to learn enough about user preferences to be able to predict what a user would read for this type of task

Use this to modify the query Most useful for repeated topic queries

*Shepherd, M., C. Watters, and A. Marath. Adaptive Filtering for Electronic News. Proc. of HICSS’35. Jan 7-10, 2002.


Type II Tasks - Ludic Tasks

The ludic theoretical perspective is based on the assumption that the reading itself brings satisfaction to the the reader.

The process of getting information is satisfying

Web browsing News reading


Ludic Use characteristics

individual path selection – Users are happy to get different information for

same general search Apperception

– Users choose information that fits their current knowledge

Habitualness– Users perform these searches as part of their

routine (rather than a specific one time info need)


Types of Ludic Tasks

Updating & community awareness tasks– What is happening?– Breadth important– Unknown events are relevant

Search & Browse tasks– What is new/odd/interesting– Novelty is important– answers not expected– Community membership

– ***Exact query unknown


Can we predict what will be read?

We cannot predict based on past behavior what a user will chose to read or the path the user will chose to follow

– Reading the news– Browsing the web

User often multitasking– 33% of web sessions involve more than 2 topics (Spink)

How can we help the user??


Predicting for Ludic Tasks

“when you don’t know where you are going, any road will take you there!”

Lewis Carroll


What do we have to work with?

An information need expressed as a query [user] a user profile [user/system]]

– Interests– history

document content (metadata keywords genre) [derived]

Link topology [author]

Usage patterns of documents [community]

Information about current task [user]


What can we Manipulate?

I. Query

II. Matching

III. Ranking

IV. Presentation


I. Improving the Query

Longer queries are better [BelK03] Average query is 2.2 terms Type of queries (Q&A / research/ browse/refind)

Qualitity of query Modification of query Personal Profiles Stereotypes


Quality of Query

Purpose of query Separation of doc set into relevant & nonrelevant documents

How well does the language of the query fit or not fit the language of the docs

Clarity*= difference between the distribution of the terms used in the query and in the distribution of all terms in the collection

*Croft, 2001


Example of Clarity Values

Query = Apple– General news DL: Clarity value is low

Apple pies, computers, city

– Computer DL: clarity value is high

• Query = Apple Computer Company• General news DL: Clarity is high• Computer DL: same as just Apple


Query Modification

Feedback: add terms from similar docs– User relevance judgements – More like this one

Profiles: add terms from history of user or user interests

Thesaurus: add related terms


Rocchio Feedback


II. Improving the Matching

Using Profiles– [Joe: railway, steam, engine, track, Europe]

Using metadata– Mapping to controlled vocabularies– Add semantics to documents– [D1:<loc>Europe</loc> <topic>Train Transportation</topic>]

Genre– Reports– Home pages– Shopping pages– News


III. Improving ranking

User Profiles Location Stereotypes


User Profiles: Recommender Systems

To “recommend” an existing path through an information space that best satisfies the user’s information need.

Depends on goal of search!


Community & Personal Profiles

Community profiles – provide stability– Common interests

Personal Profiles– Long term interests vs short term– Multiple interests– Topic drift


Effect for Browsing Tasks

Browsing behavior was idiosyncratic and personal System could not learn over time

*Shepherd, M., C.Watters, and R.Kaushik. Lessons from Reading E-News for Browsing the Web: The Roles of Genre and Task. Proc. of the Annual Conference of the American Society for Information Science and Technology. November 2001, Washington.


Profiles & Tasks

Repeated queries based on user profile for sustained interests work well

Feedback mechanisms such as Rocchio work well for sustained interests

BUT not for idiosyncratic queries or browsing


Example of Alternate Ranking:Geospatial Queries

What is here?– What can I do in Bangalore?

Where is there x?– Where can I ski in Eastern Canada or USA?


Where can I go skiing?


What do we have to work with

Geoparsing– Recognizing geographical context – country, river, feature etc

Geocoding– Assigning longitude and latitude values– End, middle, etc


User Stereotypes for Medical News*

Select medical items from online news sources

Categorize medical items by intended audience

*Watters,C., W.Zheng, and E.Milios. 2002. Filtering for Medical News Items. Proc. of the American Society for Information and Technology Conference. Nov. 15-19, Pittsburgh.


Profiling by Keyword

Customized vocabulary in MeSH Pruned non medical branches

– 31, 441 headings

Assigned weights to these headings

nonmedical 1 building

Lay medical 2 Body,stomach

General med 3 Anatomy,umbilicus

Specific med 4 Inguinal Canal


Prototype


IV. Improving Presentation

1.Genre

2. Views

3. Transformations for device


Hit List(linear)


News (broadsheet)


Album

Preferred broadsheet for browsing

*Shneiderman’s PhotoLib


Report


Effect of Genre on Different Devices


Views

*R.Furuta


Million list server messages

*websom.hut.fi/websom


Summary of Interventions

Improve the query Improve matching Improve the ranking Improve presentation

profiles

querymatch rank present

user


Reality Check

Not so easy for real tasks and real users– Topic shifts– Topic relevancy/ importance judgements– *Multitasking– *Task Detection– *Getting Personal Preferences


Can we help?

YES – Queries

can be modified for U&G tasks Use of community profiles for Ludic tasks

– Alternative ranking schemes can be based on type of task

– Match presentation to contents and use


Roles of the DL

Conflicting Goals– Archival – Access – Derivative uses (ex. Animation)– Digital rights management


Conclusions for Digital Libraries

User is an integral part of the system User’s immediate task and motivation matters Community interests matter Ranganathan’s rule 2 for 2003

– Every user his or her information.


Thank you!

More information: My web site

– www.cs.dal.ca/~watters

Web Information Filtering Lab– www.cs.dal.ca/wifl

Dec 2003, DRTC© C.Watters 1 Users and the Digital Library Carolyn Watters Dalhousie University...

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