personalized information services
DESCRIPTION
Personalized Information Services. Javed Mostafa Indiana 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. - PowerPoint PPT PresentationTRANSCRIPT
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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Sessions
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rma
lize
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rec
isio
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Concrete
Middle
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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Sessions
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cisi
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low -to-hi
hi-to-low
hybridchange
Abrupt Interest Change
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1 6 11 16 21 26 31 36 41
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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11 6 11 16 21 26 31 36 41
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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1 2 3 4
Stages
No
rmal
ized
Pre
cisi
on
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: [email protected]