April 11, 2003

Personalized Information Services

Javed MostafaIndiana University, Bloomington

Outline Personalization as part of a broader field Personalization vs. customization

Representation A research issue in personalization Approaches taken to study the issue

Results Conclusion

Acknowledgment:The research described in this presentation is a collaboration among a number of people. I am grateful forwork conducted by Dr. Mukhopadhyay & Dr. Palakal (Computer & Information Science, IUPUI). I am alsoindebted to two of my previous students: Luz Quiroga and Junliang Zhang. Thanks also to NSF for fundingthis research.

Connection to a broader field Personalization is part of a larger field known as context

aware computing (CAC)

CAC is concerned with a broad range of problems including development of smart environments (offices, homes, cars, etc.), smart weapons and appliances, smart clothing, and information systems

Some interesting projects: Project Oxygen (MIT): http://oxygen.lcs.mit.edu/Overview.html SmartSpaces (NIST): http://www.nist.gov/smartspace/smartSpaces/ Adaptive Systems: Attentive User Interfaces

(Microsoft): http://www.research.microsoft.com/adapt/

Context Aware Information Services (CAIS)

Goal: Basic information “support” services (i.e., browse, search, filter, presentation and visualization) should be: seamlessly available from any location, any device, or any application, and in a form that permits optimum use of the information

Context Aware Information Services (CAIS) Context is complex

Users can interact with: a variety of info systems: their desktop, a

laptop, a handheld, or a palmtop

A variety of applications and documents

Users may be stationary or mobile

Levels in CAIS

MS-WordMS-Excel

Photoshop

Netscape

Different types of documents and content

Desktop Tablet PDA

Users interaction, users short term demands, user’ s long term needs

Requirements: Proactive awareness and responses

Proactively seek information related to content being manipulated by the user and bring related and relevant information to the user’s attention

Automatically modulate the features and presentation according to device and application characteristics

Contexts of a Typical UserLocation

Applications

Tasks

Immediate and long-term info demands

Device

Information

Customization vs. Personalization Customization = taking into account contexts

other than those that represent personal information demands and interests (short- or long- term)

Personalization = taking into account contextual information related to user’s information demands and interests (e.g., query terms, relevance feedback on documents, rating, etc.)

Both, together, support context aware information services

Information

Customization vs. Personalization

Location

Applications

Tasks

Immediate and long-term info demands

DeviceRepresentationfor customization

Representationfor personalization

Representation

To provide context aware info services requires maintaining up-to-date contextual information in a form that permits efficient computation and accurate predictions about user’s info needs, i.e., need context representation

Representation for Personalization: User Profile

We developed a representation to predict relevance of new information according to user’s interest and long-term information need Requirements supported:

Online learning Low latency Permits exploration and adaptation

Generating the representation

To generate the representation we relied on rating or indicators of interest on topical categories

The representation contained two types of information: topical categories and assessment of interest in the categories

Interest representation for personalization

Categories

c1

c2

c3

::cn

u1

u2

u3

::un

t1

t2

t3

::tn

Probability that category 2 is themost relevant category

Probability that category 1 isrelevant to the user

Top class Relevance of categories

User profile/model

Documents

Source of interest information Explicit: User’s were asked to provide rating on

documents

Implicit: User’s interaction with content and the interface were taken into consideration

Such interest information was converted into the (two-level) profile/model by using a simple RL algorithm:

Mostafa et al. A multilevel approach to intelligent information filtering: Model, system, and evaluation. ACM TOIS, 15(4), 1997.

Different applications have been created, incl. SIMSIFTER and TuneSIFTER

See: lair.indiana.edu/research/

Research issue: Big picture

Interested in two types of research issues:

With any type of intelligent HCI a fundamental issue is control Who is in charge? If the user wishes to delegate, how much autonomy should the system

have?

Agent vs. User (Direct Manipulation) Maes & Shneiderman debate: http://www.acm.org/sigchi/chi97/proceedings/panel/jrm.htm

If the user wishes to take charge, how much responsibility should the user take on? : User effort … user involvement can impact system effectiveness

A research issue: User’s Role in Personalization

Type of interest

Interest change

User Involvement Amount of interaction Type of interaction

Approaches to study the research issue

As it is v. difficult to manipulate certain conditions (e.g., change of interest w.r.t. certain topics) we developed a simulation tool

For other conditions we conducted experimental studies with actual users

Simulation study using SIMSIFTER

Type of interest may impact the rating (degree and frequency)

Rating may impact how quickly the system can “learn” or generate an accurate profile

Accuracy of profile determines accuracy of prediction of relevance

SIMSIFTER used about 1.4K consumer health documents and 15 categories of health information (anxiety, allergy, heart, cholesterol, depression, diet, environment, exercise, eye, headache, lung, medicine, teeth, men-health, and women-health )

Study: Different Profile Types

We created different types of profiles – concrete, middle, and mild-low

Degree of interest was used to generate rating probabilistically Frequency of rating increases with

increased intensity of interest

Results: Different Profile Types

Different Interest Types

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Mildlow

Nolearning

Impact of different types of interest on prediction of relevance

Study: Change in Interest

Over time as the user is exposed to continuous flow of new information and user’s situation changes, the user may experience change in interest

Change in interest may be gradual or abrupt

Results: Change in Interest

Incremental Interest Change

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hybridchange

Abrupt Interest Change

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Sessions

suddendev

suddendevloss

suddendevlossdev

Impact of change in interest on prediction of relevance

Study: Modalities of interest information collection

Interest information can be collected explicitly by asking the user

By generating the rating based on content viewed by the user

Or, a combination of both of the above strategies

Results: Different modalities of interest information collection

Different Sources of Interest Data

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Sessions

rating

initplusrating

Impact of different interest information collection modalities on prediction of relevance

TuneSIFTER Study Aim was to engage actual users and analyze

different modalities of interest information collection

Rule-based Explicitly by requiring users to rate Implicitly by observing behavior and associating

behavior with rating

Provided access to music titles in a dozen genre from the MP3.com service

35 subjects recruited from IUB

TuneSIFTER User Interface

Study: Three modalities of interest information collection Rule based = user provided the profile in the

first session

Explicit learning = user rated music titles

Implicit learning = different sources used: user’s click on the column of title, user’s click on the column of artist name, user’s click on the column of genre, and user’s click on the column to request more information. In addition, the time user spent on listening to the music was also recorded by the implicit-learning system

Results: Three modalities of interest information collection

NP values accross four stages

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Stages

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Implicit Explicit

Rule-based No profile

Conclusions The representation and the learning

approach developed are quite robust in terms of capturing different types of interest and change in interest

Implicit modality, when time data is available, may be applicable in reducing user involvement without sacrificing performance

Limitations and Future Work User involvement may vary with tasks and

domains For example Kelly and Belkin (2002) state that

reading time is not a reliable source for implicit modeling

Different levels of modeling may be needed Topical granularity in the user profile influences

performance – Quiroga and Mostafa (2002) Two-level modeling needed in the News domain

(content highly dynamic)

Additional Citations

Kelly and Belkin. Modeling characteristics of the User’s Problematic Situation with Information Search and Use Behaviors. JCDL Workshop on Document Search Interface Design, http://xtasy.slis.indiana.edu/jcdlui/uiws.html, 2002.

Quiroga and Mostafa. An Experiment in Building Profiles in Information Filtering: The Role of Context of User Relevance Feedback. Information Processing & Management, 38(5), 2002.

Pitkow et al. Personalized Search. CACM, 45(9), 2002.

User modeling 10th Anniversary Issue. Gerhard Fischer’s work in this area is especially recommended.

Related IR Forums

SIGIR - ACM Special Interest Group on Information Retrieval Conference

UIST  - ACM User Interface Software & Technology Conference UIU - ACM Intelligent User Interfaces Conference TREC - Text REtrieval Conference ASIST - American Society for Information Science and

Technology Conference JCDL - Joint Conference on Digital Libraries CIKM - Conference on Information and Knowledge

Management AGENTS - International Conference on Autonomous

Agents

Need more information?

Our lab:

Laboratory of Applied Informatics Research (lair.indiana.edu)

Email: jm@indiana.edu